Fundamental Chaos; Stable World

Before continuing to bring my QM-related tweets here, I think I need to give some way (even if very cartoonish) to help visualize the kind of physics I have in mind for my approach to QM. But before I am able to do that, I need to introduce (at least some of) you to my way of thinking on these matters. An important consideration in this regard is what I will cover in this post, so that eventually (may be after one more post or so) we could come back to my tweets on QM proper.

Chaos in fundamental theory vs. stability evident in world:

If the physical reality at its deepest level—i.e. at the quantum mechanical level—shows, following my new approach [^], a nonlinear dynamics, i.e. catastrophic changes that can be characterized as being chaotic, then how come the actual physical world remains so stable? Why is it so boringly familiar-looking a place? … A piece of metal like silver kept in a cupboard stays just that way for years together, even for centuries. It stays the same old familiar object; it doesn’t change into something unrecognizable. How come?

The answer in essence is: Because of the actual magnitudes of the various quantities that are involved in the dynamics, that’s why.

To help understand the answer, it is best to make appeal not directly to quantum-mechanical calculations or even simulations, but, IMHO, to the molecular dynamics simulations.

QM calculations are often impossible and simulations are very hard:

Quantum-mechanical calculations can’t handle the nonlinear QM (of the kind proposed by me), full stop.

For that matter, even if you follow the mainstream QM (as given in textbooks), calculations are impossible for even the simplest possible systems like just two interacting electrons in an infinite potential box. No wonder even a single helium atom taken in isolation poses a tough challenge for it [^]. With all due respect (because it was as far back as in 1929), all that even Hylleraas [^] could manage, by way of manual calculations (with a mechanical calculator) was determination of only the energy of the ground-state of the He atom, not a direct expression for its wave-function—including, of course, the latter’s time-dependence.

In fact, even simulations today have to go through a lot of hoops—the methods to simulate QM are rather indirect. They don’t even aim to get out wavefunctions as the result.

Even the density functional theory (DFT), a computational technique that got its inventors a physics Nobel [^], introduces and deals with only the electron density, not wavefunctions proper—in case you never noticed it. Personally, in my limited searches, I haven’t found anyone giving even an approximate expression for the Helium wavefunction itself. [If someone can point me to the literature, please do; thanks in advance!]

To conclude, quantum-mechanical calculations are so tough that direct simulations are not available for even such simplest wavefunctions as that of the helium atom. And mind you, helium is the second simplest atom in the universe, and it’s just an atom—not a molecule, a nano-structure, or a photo-multiplier tube.

Molecular dynamics as a practically accessible technique:

However, for developing some understanding of how systems are put together and work at the atomic scale, we can make use of the advances made over the past 60–70 years of computational modeling. In particular, we can derive some very useful insights by using the molecular dynamics simulations [^] (MD for short).

It is a fairly well-established fact that MD simulations, although classical in nature, are sufficiently close to the ab initio quantum-mechanical calculations too, as to be practically quite useful. They have proved their utility for at least some “simpler” systems and for certain practical purposes, especially in the condensed matter physics (even if not for more ambitious goals like automated drug discovery).

See the place of MD in the various approaches involved in the multi-scale modeling, here. [^]. Note that MD is right next to QM.

So, we can use MD simulations in order to gain insights into our above question, viz. the in-principle nonlinearity at the most basic level of QM vs. the obvious stability of the real world.

Some details of the molecular dynamics technique:

In molecular dynamics, what you essentially have are atomic nuclei, regarded as classical point-particles, which interact with each other via a classical “springy” potential. The potential goes through a minimum, i.e., a point of equilibrium (where the forces are zero).

Imagine two hard steel balls connected via a mechanical spring that has some neutral length. If you compress the balls together, the spring develops forces which try to push the balls apart. If you stretch the balls apart, the spring develops opposite kind of forces which tend to pull the balls back together. Due to their inertia, when the balls are released from an initial position of a stretch/compression, the balls can overshoot the intermediate position of neutrality, which introduces oscillations.

The MD technique is somewhat similar. In the simple balls + mechanical spring system discussed above, the relation of force vs. separation is quite simple: it’s linear. $F = -k(x - x_0)$. In contrast, The inter-nucleus potential used in the molecular dynamics is more complicated. It is nonlinear. However, it still has this feature of a potential valley, which implies a neutral position. See the graph of the most often used potential, viz., the Lennard-Jones potential [^].

In conducting the MD simulation, you begin with a large number of such nuclei, taken as the classical point-particles of definite mass. Although in terms of the original idea, these point-particles represent the nuclei of atoms (with the inter-nuclear potential field playing the role of the $\Psi$ wavefunction), the literature freely uses the term “atoms” for them.

The atoms in an MD simulation are given some initial positions (which do not necessarily lie at the equilibrium separations), and some initial velocities (which are typically random in magnitude and direction, but falling into a well-chosen band of values). The simulation consists of following Newton’s second law: $F = ma$. Time is discretized, typically in steps of uniform durations. Forces on atoms are calculated from their instantaneous separations. These forces (accelerations) are integrated over a time-step to obtain velocities, which are then again integrated over time to obtain changes in atomic positions over the time-step. The changed positions imply changes in instantaneous forces, and thus makes the technique nonlinear. The entire loop is repeated at each time-step.

As the moving atoms (nuclei) change their positions, the system of their overall collection changes its configuration.

If the numerical ranges of the forces / accelerations / velocities / displacements are small enough, then even if the nuclei undergo changes in their positions with the passage of a simulation, their overall configuration still remains more or less the same. The initially neighbouring atoms remain in each other’s neighbourhood, even if individually, they might be jiggling a little here and there around their equilibrium positions. Such a dynamical state in the simulation corresponds to the solid state.

If you arrange for a gradual increase in the velocities (say by effecting an increase in an atom’s momentum when it bumps against the boundaries of the simulation volume, or even plain by just adding a random value from a distribution to the velocities of all the atoms at regular time intervals), then statistically, it is the same as increasing the temperature of the system.

When the operating velocities become large enough (i.e. when the “temperature” becomes high enough), the configuration becomes such that the moving atoms can now slip past their previous neighbours, and form a new neighbourhood around a new set of atoms. However, their velocities are still small enough that their overall assembly does not “explode;” the assembly continues to occupy roughly the same volume, though it may change its shape. Such a dynamic state corresponds to the liquid state.

Upon further increasing the temperature (i.e. velocities), the atoms now begin to dash around with such high speeds that they can overcome the pull of their neighbouring atoms, and begin to escape into the empty space outside the assemblage. The assembly taken as a whole ceases to occupy a definite sub-region inside the simulation chamber. Instead, the atoms now shoot across the entire chamber. Of course this is nothing but the gaseous state. Impermeable boundaries have to be assumed to keep the atoms inside the finite region of simulation. (Actually, similar, even for the liquid state.) The motion of the atoms in the gaseous phase looks quite chaotic, even if in a statistical sense, certain macro-level properties like pressure are being maintained lawfully. The kinetic energy, in particular, stays constant for an isolated system (within the numerical errors) even in the gaseous state.

While there are tons of resources on the ‘net for MD, here is one particularly simple but accurate enough a Python code, with good explanations [^]. I especially liked it because unlike so many other “basic” MD codes, this one even shows the trick of shifting the potential so as to effectively cut it off to a finite radius in a smooth manner—many introductory MD codes do it rather crudely, by directly cutting off the potential (thereby leaving a discontinuity of the vertical jump in the potential field). [In theory, the potential goes to infinity, but in practice, you need to cut it off just so as to keep the computational costs down.]

Here is a short video showing an MD simulation of melting of ice (and I guess, also of evaporation) [^].  It has taken into account the dipole nature of the water molecules too.

The entire sequence can of course be reversed. You can always simulate a gas-to-liquid transition, and further, you can also simulate solidification. Here is a video showing the reverse phase-change for water: [^]

The points to gather from the MD simulations, for our purposes:

MD simulations of even gases retains a certain orderliness. Despite all the random-looking motions, they still are completely lawful, and the laws are completely deterministic. The liquid state certainly shows much a better degree of orderliness as compared to that in the gaseous state. The solid state shows some remnants of the random-looking motion, but these motions are now very highly localized, and so, the assemblage as a whole looks very orderly, stable. It not only preserves its volume, it wouldn’t show even just “flowing” if you were to program the simulation to have a tilting of the simulation chamber incorporated in it.

Now the one big point I want you to note is this.

Even in the very stable, orderly looking simulation of the solid state, the equations governing the dynamics of all the individual atoms still are nonlinear [^], i.e., they still are chaotic. It is the chaotic equations which produce the very orderly solid state.

MD simulations would not be capable of simulating phase transitions (from solid to liquid to gas etc.) using just identical balls interacting via simple pair-wise interactions, if the basic equations didn’t have a nonlinearity built into them. [I made a tweet to this effect last month, on 02 August 2019.]

So, even the very stable-looking solid state is maintained by the assemblage only by following the same nonlinear dynamical laws as required which allow phase transitions to occur and show the evident randomness for the gaseous state.

It’s just that, when the the parameters like velocity and acceleration (determined by the potential) fall into certain ranges of small enough values, then even if the governing equation remains nonlinear, the dynamical state automatically gets confined to a regime of the highly orderly and stable solid state.

So, the lesson is:

Even if the dynamical nonlinearity in the governing laws sure implies instabilities in principle, what matters is the magnitudes of parameters (here, prominently, the velocities i.e. the “temperature” of simulation). The operative numerical magnitudes of the parameters directly determine the regime of the dynamical state. The regime can correspond to a very highly ordered state too.

Ditto, for the actual world.

In fact, something stronger can be said: If the MD equations were not to be nonlinear, if they were not to be chaotic, then they would fail to reproduce not only phase transitions (like solid to liquid etc.), but also such utterly orderly behaviour as the constancy of the temperature for these phase-transitions. Even stronger: The numerical values of the parameters don’t have to be exactly equal to some critical values. Even if the parameter values vary a lot, so long as they fall into a certain range, the solution regime (the qualitative behaviour of the solution) remains unchanged, stable!

In MD, chaotic equations not only ensure that phase transitions like melting can at all occur in a simulation, their specific nature even ensures the exact constancy of the temperature for phase changes. Parameter values aren’t even required to be exactly equal to some critical values; they can vary a lot (within a certain range), and even then, the solution regime would remain unchanged—it would still show the stability of the qualitative behaviour.

Ditto, for the quantum mechanical simulations using my new approach. (Though I haven’t done a simulation, the equations show a similar kind of a nonlinearity.)

In my approach, quantum mechanical instability is ever present in each part of the physical world. However, the universe that we live in simply exists (“has been put together”) in such a way that the numerical values of the parameters actually operative are such that the real world shows the same feature of stability, as in the MD simulations.

The example of a metal piece left alone for a while:

If you polish and buff, or chemically (or ultrasonically) clean, a piece of metal to a shining bright state, and then leave it alone for a while, it turns dull over a period of time. That’s because of corrosion.

Corrosion is, chemically, a process of oxidation. Oxygen atoms from the air react with those pure metal atoms which are exposed at a freshly polished surface. This reaction is, ultimately, completely governed by the laws of quantum mechanics—which, in my approach has a nonlinearity of a specific kind. Certain numerical parameters control the speed with which the quantum-mechanical rearrangements of the wavefunction governing the oxygen and metal atoms proceeds.

The world we live in happens to carry those values for such parameters that corrosion turns out to be a slow process. Also, it turns out to be a process that mostly affects only the surface of a metal (in the solid state). The dynamical equations of quantum mechanics, even if nonlinear, are such that the corrosion cannot penetrate very deep inside the metal—the values of the governing parameters are such that oxygen atoms cannot diffuse so easily into the interior regions all that easily. If the metal were to be in a liquid state, it would be altogether a different matter—again, another range of the nonlinear parameters, that’s all.

So, even if it’s a nonlinear (“chaotic” or even “randomness-possessing”) quantum-mechanical evolution, the solid metal piece does not corrode all the way through.

That’s why, you can always polish your family silver, or the copper utensils used in “poojaa” (worship), and use them again and again.

The world is a remarkably stable place to be in.

Concluding…:

So what’s the ultimate physics lesson we can draw from this story?

It’s this:

In the end, the qualitative nature of the solutions to physical problems is not determined solely by the kind of mathematical laws which do govern that phenomenon; the nature of the constituents of a system (what kind of objects there are in it), the particulars of their configurations, and the numerical ranges of the parameters as are operative during their interactions, etc., also matter equally well—if not even more!

Don’t fall for those philosophers, sociologists, humanities folks in general (and even some folks employed as “scientists”) who snatch some bit of the nonlinear dynamics or chaos theory out of its proper context, and begin to spin a truly chaotic yarn of utter gibberish. They simply don’t know any better. That’s another lesson to be had from this story. There is a huge difference between the cultural overtones associated with the word “chaos” and the meaning the same term has in absolutely rigorous studies in physics. Hope I will never have to remind you on this one point.

A song I like:

(Marathi) “shraavaNaata ghana niLaa barasalaa…”
Music: Shreenivas Khale
Singer: Lata Mangeshkar
[Credits listed, happily, in a random order.]

A list of books for understanding the non-relativistic QM

TL;DR: NFY (Not for you).

In this post, I will list those books which have been actually helpful to me during my self-studies of QM.

But before coming to the list, let me first note down a few points which would be important for engineers who wish to study QM on their own. After all, my blog is regularly visited by engineers too. That’s what the data about the visit patterns to various posts says.

Others (e.g. physicists) may perhaps skip over the note in the next section, and instead jump directly over to the list itself. However, even if the note for engineers is too long, perhaps, physicists should go through it too. If they did, they sure would come to know a bit more about the kind of background from which the engineers come.

I. A note for engineers who wish to study QM on their own:

The point is this: QM is vast, even if its postulates are just a few. So, it takes a prolonged, sustained effort to learn it.

For the same reason (of vastness), learning QM also involves your having to side-by-side learn an entirely new approach to learning itself. (If you have been a good student of engineering, chances are pretty good that you already have some first-hand idea about this meta-learning thing. But the point is, if you wish to understand QM, you have to put it to use once again afresh!)

In terms of vastness, QM is, in some sense, comparable to this cluster of subjects spanning engineering and physics: engineering thermodynamics, statistical mechanics, kinetics, fluid mechanics, and heat- and mass-transfer.

I.1 Thermodynamics as a science that is hard to get right:

The four laws of thermodynamics (including the zeroth and the third) are easy enough to grasp—I mean, in the simpler settings. But when it comes to this subject (as also for the Newtonian mechanics, i.e., from the particle to the continuum mechanics), God lies not in the postulates but in their applications.

The statement of the first law of thermodynamics remains the same simple one. But complexity begins to creep in as soon as you begin to dig just a little bit deeper with it. Entire categories of new considerations enter the picture, and the meaning of the same postulates gets both enriched and deepened with them. For instance, consider the distinction of the open vs. the closed vs. the isolated systems, and the corresponding changes that have to be made even to the mathematical statements of the law. That’s just for the starters. The complexity keeps increasing: studies of different processes like adiabatic vs. isochoric vs. polytropic vs. isentropic etc., and understanding the nature of these idealizations and their relevance in diverse practical applications such as: steam power (important even today, specifically, in the nuclear power plants), IC engines, jet turbines, refrigeration and air-conditioning, furnaces, boilers, process equipment, etc.; phase transitions, material properties and their variations; empirical charts….

Then there is another point. To really understand thermodynamics well, you have to learn a lot of other subjects too. You have to go further and study some different but complementary sciences like heat and mass transfer, to begin with. And to do that well, you need to study fluid dynamics first. Kinetics is practically important too; think of process engineering and cost of energy. Ideas from statistical mechanics are important from the viewpoint of developing a fundamental understanding. And then, you have to augment all this study with all the empirical studies of the irreversible processes (think: the boiling heat transfer process). It’s only when you study such an entire gamut of topics and subjects that you can truly come to say that you now have some realistic understanding of the subject matter that is thermodynamics.

Developing understanding of the aforementioned vast cluster of subjects (of thermal sciences) is difficult; it requires a sustained effort spanning over years. Mistakes are not only very easily possible; in engineering schools, they are routine. Let me illustrate this point with just one example from thermodynamics.

Consider some point that is somewhat nutty to get right. For instance, consider the fact that no work is done during the free expansion of a gas. If you are such a genius that you could correctly get this point right on your very first reading, then hats off to you. Personally, I could not. Neither do I know of even a single engineer who could. We all had summarily stumbled on some fine points like this.

You see, what happens here is that thermodynamics and statistical mechanics involve entirely different ways of thinking, but they both are being introduced almost at the same time during your UG studies. Therefore, it is easy enough to mix up the some disparate metaphors coming from these two entirely different paradigms.

Coming to the specific example of the free expansion, initially, it is easy enough for you to think that since momentum is being carried by all those gas molecules escaping the chamber during the free expansion process, there must be a leakage of work associated with it. Further, since the molecules were already moving in a random manner, there must be an accompanying leakage of the heat too. Both turn out to be wrong ways of thinking about the process! Intuitions about thermodynamics develop only slowly. You think that you understood what the basic idea of a system and an environment is like, but the example of the free expansion serves to expose the holes in your understanding. And then, it’s not just thermo and stat mech. You have to learn how to separate both from kinetics (and they all, from the two other, closely related, thermal sciences: fluid mechanics, and heat and mass transfer).

But before you can learn to separate out the unique perspectives of these subject matters, you first have to learn their contents! But the way the university education happens, you also get exposed to them more or less simultaneously! (4 years is as nothing in a career that might span over 30 to 40 years.)

Since you are learning a lot many different paradigms at the same time, it is easy enough to naively transfer your fledgling understanding of one aspect of one paradigm (say, that of the particle or statistical mechanics) and naively insert it, in an invalid manner, into another paradigm which you are still just learning to use at roughly the same time (thermodynamics). This is what happens in the case of the free expansion of gases. Or, of throttling. Or, of the difference between the two… It is a rare student who can correctly answer all the questions on this topic, during his oral examination.

Now, here is the ultimate point: Postulates-wise, thermodynamics is independent of the rest of the subjects from the aforementioned cluster of subjects. So, in theory, you should be able to “get” thermodynamics—its postulates, in all their generality—even without ever having learnt these other subjects.

Yet, paradoxically enough, we find that complicated concepts and processes also become easier to understand when they are approached using many different conceptual pathways. A good example here would be the concept of entropy.

When you are a XII standard student (or even during your first couple of years in engineering), you are, more or less, just getting your feet wet with the idea of the differentials. As it so happens, before you run into the concept of entropy, virtually every physics concept was such that it was a ratio of two differentials. For instance, the instantaneous velocity is the ratio of d(displacement) over d(time). But the definition of entropy involves a more creative way of using the calculus: it has a differential (and that too an inexact differential), but only in the numerator. The denominator is a “plain-vanilla” variable. You have already learnt the maths used in dealing with the rates of changes—i.e. the calculus. But that doesn’t mean that you have an already learnt physical imagination with you which would let you handle this kind of a definition—one that involves a ratio of a differential quantity to an ordinary variable. … “Why should only one thing change even as the other thing remains steadfastly constant?” you may wonder. “And if it is anyway going to stay constant, then is it even significant? (Isn’t the derivative of a constant the zero?) So, why not just throw the constant variable out of the consideration?” You see, one major reason you can’t deal with the definition of entropy is simply because you can’t deal with the way its maths comes arranged. Understanding entropy in a purely thermodynamic—i.e. continuum—context can get confusing, to say the least. But then, just throw in a simple insight from Boltzmann’s theory, and suddenly, the bulb gets lit up!

So, paradoxically enough, even if multiple paradigms mean more work and even more possibilities of confusion, in some ways, having multiple approaches also does help.

When a subject is vast, and therefore involves multiple paradigms, people regularly fail to get certain complex ideas right. That happens even to very smart people. For instance, consider Maxwell’s daemon. Not many people could figure out how to deal with it correctly, for such a long time.

…All in all, it is only some time later, when you have already studied all these topics—thermodynamics, kinetics, statistical mechanics, fluid mechanics, heat and mass transfer—that finally things begin to fall in place (if they at all do, at any point of time!). But getting there involves hard effort that goes on for years: it involves learning all these topics individually, and then, also integrating them all together.

In other words, there is no short-cut to understanding thermodynamics. It seems easy enough to think that you’ve understood the 4 laws the first time you ran into them. But the huge gaps in your understanding begin to become apparent only when it comes to applying them to a wide variety of situations.

I.2 QM is vast, and requires multiple passes of studies:

Something similar happens also with QM. It too has relatively few postulates (3 to 6 in number, depending on which author you consult) but a vast scope of applicability. It is easy enough to develop a feeling that you have understood the postulates right. But, exactly as in the case of thermodynamics (or Newtonian mechanics), once again, the God lies not in the postulates but rather in their applications. And in case of QM, you have to hasten to add: the God also lies in the very meaning of these postulates—not just their applications. QM carries a one-two punch.

Similar to the case of thermodynamics and the related cluster of subjects, it is not possible to “get” QM in the first go. If you think you did, chances are that you have a superhuman intelligence. Or, far, far more likely, the plain fact of the matter is that you simply didn’t get the subject matter right—not in its full generality. (Which is what typically happens to the CS guys who think that they have mastered QM, even if the only “QM” they ever learnt was that of two-state systems in a finite-dimensional Hilbert space, and without ever acquiring even an inkling of ideas like radiation-matter interactions, transition rates, or the average decoherence times.)

The only way out, the only way that works in properly studying QM is this: Begin studying QM at a simpler level, finish developing as much understanding about its entire scope as possible (as happens in the typical Modern Physics courses), and then come to studying the same set of topics once again in a next iteration, but now to a greater depth. And, you have to keep repeating this process some 4–5 times. Often times, you have to come back from iteration n+2 to n.

As someone remarked at some forum (at Physics StackExchange or Quora or so), to learn QM, you have to give it “multiple passes.” Only then can you succeed understanding it. The idea of multiple passes has several implications. Let me mention only two of them. Both are specific to QM (and not to thermodynamics).

First, you have to develop the art of being able to hold some not-fully-satisfactory islands of understanding, with all the accompanying ambiguities, for extended periods of time (which usually runs into years!). You have to learn how to give a second or a third pass even when some of the things right from the first pass are still nowhere near getting clarified. You have to learn a lot of maths on the fly too. However, if you ask me, that’s a relatively easier task. The really difficult part is that you have to know (or learn!) how to keep forging ahead, even if at the same time, you carry a big set of nagging doubts that no one seems to know (or even care) about. (To make the matters worse, professional physicists, mathematicians and philosophers proudly keep telling you that these doubts will remain just as they are for the rest of your life.) You have to learn how to shove these ambiguous and un-clarified matters to some place near the back of your mind, you have to learn how to ignore them for a while, and still find the mental energy to once again begin right from the beginning, for your next pass: Planck and his cavity radiation, Einstein, blah blah blah blah blah!

Second, for the same reason (i.e. the necessity of multiple passes and the nature of QM), you also have to learn how to unlearn certain half-baked ideas and replace them later on with better ones. For a good example, go through Dan Styer’s paper on misconceptions about QM (listed near the end of this post).

Thus, two seemingly contradictory skills come into the play: You have to learn how to hold ambiguities without letting them affect your studies. At the same time, you also have to learn how not to hold on to them forever, or how to unlearn them, when the time to do becomes ripe.

Thus, learning QM does not involve just learning of new contents. You also have learn this art of building a sufficiently “temporary” but very complex conceptual structure in your mind—a structure that, despite all its complexity, still is resilient. You have to learn the art of holding such a framework together over a period of years, even as some parts of it are still getting replaced in your subsequent passes.

And, you have to compensate for all the failings of your teachers too (who themselves were told, effectively, to “shut up and calculate!”) Properly learning QM is a demanding enterprise.

II. The list:

Now, with that long a preface, let me come to listing all the main books that I found especially helpful during my various passes. Please remember, I am still learning QM. I still don’t understand the second half of most any UG book on QM. This is a factual statement. I am not ashamed of it. It’s just that the first half itself managed to keep me so busy for so long that I could not come to studying, in an in-depth manner, the second half. (By the second half, I mean things like: the QM of molecules and binding, of their spectra, QM of solids, QM of complicated light-matter interactions, computational techniques like DFT, etc.) … OK. So, without any further ado, let me jot down the actual list.  I will subdivide it in several sub-sections

II.0. Junior-college (American high-school) level:

Obvious:

• Resnick and Halliday.
• Thomas and Finney. Also, Allan Jeffrey

II.1. Initial, college physics level:

• “Modern physics” by Beiser, or equivalent
• Optional but truly helpful: “Physical chemistry” by Atkins, or equivalent, i.e., only the parts relevant to QM. (I know engineers often tend to ignore the chemistry books, but they should not. In my experience, often times, chemistry books do a superior job of explaining physics. Physics, to paraphrase a witticism, is far too important to be left to the physicists!)

II.2. Preparatory material for some select topics:

• “Physics of waves” by Howard Georgi. Excellence written all over, but precisely for the same reason, take care to avoid the temptation to get stuck in it!
• Maths: No particular book, but a representative one would be Kreyszig, i.e., with Thomas and Finney or Allan Jeffrey still within easy reach.
• There are a few things you have to relearn, if necessary. These include: the idea of the limits of sequences and series. (Yes, go through this simple a topic too, once again. I mean it!). Then, the limits of functions.
Also try to relearn curve-tracing.
• Unlearn (or throw away) all the accounts of complex numbers which remain stuck at the level of how $\sqrt{-1}$ was stupefying, and how, when you have complex numbers, any arbitrary equation magically comes to have roots, etc. Unlearn all that talk. Instead, focus on the similarities of complex numbers to both the real numbers and vectors, and also their differences from each. Unlike what mathematicians love to tell you, complex numbers are not just another kind of numbers. They don’t represent just the next step in the logic of how the idea of numbers gets generalized as go from integers to real numbers. The reason is this: Unlike the integers, rationals, irrationals and reals, complex numbers take birth as composite numbers (as a pair of numbers that is ordered too), and they remain that way until the end of their life. Get that part right, and ignore all the mathematicians’ loose talk about it.
Study complex numbers in a way that, eventually, you should find yourself being comfortable with the two equivalent ways of modeling physical phenomena: as a set of two coupled real-valued differential equations, and as a single but complex-valued differential equation.
• Also try to become proficient with the two main expansions: the Taylor, and the Fourier.
• Also develop a habit of quickly substituting truncated expansions (i.e., either a polynomial, or a sum complex exponentials having just a few initial harmonics, not an entire infinity of them) into any “arbitrary” function as an ansatz, and see how the proposed theory pans out with these. The goal is to become comfortable, at the same time, with a habit of tracing conceptual pathways to the meaning of maths as well as with the computational techniques of FDM, FEM, and FFT.
• The finite differences approximation: Also, learn the art of quickly substituting the finite differences ($\Delta$‘s) in place of the differential quantities ($d$ or $\partial$) in a differential equation, and seeing how it pans out. The idea here is not just the computational modeling. The point is: Every differential equation has been derived in reference to an elemental volume which was then taken to a vanishingly small size. The variation of quantities of interest across such (infinitesimally small) volume are always represented using the Taylor series expansion.
(That’s correct! It is true that the derivations using the variational approach don’t refer to the Taylor expansion. But they also don’t use infinitesimal volumes; they refer to finite or infinite domains. It is the variation in functions which is taken to the vanishingly small limit in their case. In any case, if your derivation has an infinitesimall small element, bingo, you are going to use the Taylor series.)
Now, coming back to why you must learn develop the habit of having a finite differences approximation in place of a differential equation. The thing is this: By doing so, you are unpacking the derivation; you are traversing the analysis in the reverse direction, you are by the logic of the procedure forced to look for the physical (or at least lower-level, less abstract) referents of a mathematical relation/idea/concept.
While thus going back and forth between the finite differences and the differentials, also learn the art of tracing how the limiting process proceeds in each such a case. This part is not at all as obvious as you might think. It took me years and years to figure out that there can be infinitesimals within infinitesimals. (In fact, I have blogged about it several years ago here. More recently, I wrote a PDF document about how many numbers are there in the real number system, which discusses the same idea, from a different angle. In any case, if you were not shocked by the fact that there can be an infinity of infinitesimals within any infinitesimal, either think sufficiently long about it—or quit studying foundations of QM.)

II.3. Quantum chemistry level (mostly concerned with only the TISE, not TDSE):

• Optional: “QM: a conceptual approach” by Hameka. A fairly well-written book. You can pick it up for some serious reading, but also try to finish it as fast as you can, because you are going to relean the same stuff once again through the next book in the sequence. But yes, you can pick it up; it’s only about 200 pages.
• “Quantum chemistry” by McQuarrie. Never commit the sin of bypassing this excellent book.
A suggestion: Once you finish reading through this particular book, take a small (40 page) notebook, and write down (in the long hand) just the titles of the sections of each chapter of this book, followed by a listing of the important concepts / equations / proofs introduced in it. … You see, the section titles of this book themselves are complete sentences that encapsulate very neat nuggets. Here are a couple of examples: “5.6: The harmonic oscillator accounts for the infrared spectrum of a diatomic molecule.” Yes, that’s a section title! Here is another: “6.2: If a Hamiltonian is separable, then its eigenfunctions are products of simpler eigenfunctions.” See why I recommend this book? And this (40 page notebook) way of studying it?
• “Quantum physics of atoms, molecules, solids, nuclei, and particles” (yes, that’s the title of this single volume!) by Eisberg and Resnick. This Resnick is the same one as that of Resnick and Halliday. Going through the same topics via yet another thick book (almost 850 pages) can get exasperating, at least at times. But guess if you show some patience here, it should simplify things later. …. Confession: I was too busy with teaching and learning engineering topics like FEM, CFD, and also with many other things in between. So, I could not find the time to read this book the way I would have liked to. But from whatever I did read (and I did go over a fairly good portion of it), I can tell you that not finishing this book was a mistake on my part. Don’t repeat my mistake. Further, I do keep going back to it, and may be as a result, I would one day have finished it! One more point. This book is more than quantum chemistry; it does discuss the time-dependent parts too. The only reason I include it in this sub-section (chemistry) rather than the next (physics) is because the emphasis here is much more on TISE than TDSE.

II.4. Quantum physics level (includes TDSE):

• “Quantum physics” by Alastair I. M. Rae. Hands down, the best book in its class. To my mind, it easily beats all of the following: Griffiths, Gasiorowicz, Feynman, Susskind, … .
Oh, BTW, this is the only book I have ever come across which does not put scare-quotes around the word “derivation,” while describing the original development of the Schrodinger equation. In fact, this text goes one step ahead and explicitly notes the right idea, viz., that Schrodinger’s development is a derivation, but it is an inductive derivation, not deductive. (… Oh God, these modern American professors of physics!)
But even leaving this one (arguably “small”) detail aside, the book has excellence written all over it. Far better than the competition.
Another attraction: The author touches upon all the standard topics within just about 225 pages. (He also has further 3 chapters, one each on relativity and QM, quantum information, and conceptual problems with QM. However, I have mostly ignored these.) When a book is of manageable size, it by itself is an overload reducer. (This post is not a portion from a text-book!)
The only “drawback” of this book is that, like many British authors, Rae has a tendency to seamlessly bunch together a lot of different points into a single, bigger, paragraph. He does not isolate the points sufficiently well. So, you have to write a lot of margin notes identifying those distinct, sub-paragraph level, points. (But one advantage here is that this procedure is very effective in keeping you glued to the book!)
• “Quantum physics” by Griffiths. Oh yes, Griffiths is on my list too. It’s just that I find it far better to go through Rae first, and only then come to going through Griffiths.
• … Also, avoid the temptation to read both these books side-by-side. You will soon find that you can’t do that. And so, driven by what other people say, you will soon end up ditching Rae—which would be a grave mistake. Since you can keep going through only one of them, you have to jettison the other. Here, I would advise you to first complete Rae. It’s indispensable. Griffiths is good too. But it is not indispensable. And as always, if you find the time and the inclination, you can always come back to Griffiths.

Starting sometime after finishing the initial UG quantum chemistry level books, but preferably after the quantum physics books, use the following two:

• “Foundations of quantum mechanics” by Travis Norsen. Very, very good. See my “review” here [^]
• “Foundations of quantum mechanics: from photons to quantum computers” by Reinhold Blumel.
Just because people don’t rave a lot about this book doesn’t mean that it is average. This book is peculiar. It does look very average if you flip through all its pages within, say, 2–3 minutes. But it turns out to be an extraordinarily well written book once you begin to actually read through its contents. The coverage here is concise, accurate, fairly comprehensive, and, as a distinctive feature, it also is fairly up-to-date.
Unlike the other text-books, Blumel gives you a good background in the specifics of the modern topics as well. So, once you complete this book, you should find it easy (to very easy) to understand today’s pop-sci articles, say those on quantum computers. To my knowledge, this is the only text-book which does this job (of introducing you to the topics that are relevant to today’s research), and it does this job exceedingly well.
• Use Blumel to understand the specifics, and use Norsen to understand their conceptual and the philosophical underpinnings.

II.Appendix: Miscellaneous—no levels specified; figure out as you go along:

• “Schrodinger’s cat” by John Gribbin. Unquestionably, the best pop-sci book on QM. Lights your fire.
• “Quantum” by Manjit Kumar. Helps keep the fire going.
• Kreyszig or equivalent. You need to master the basic ideas of the Fourier theory, and of solutions of PDEs via the separation ansatz.
• However, for many other topics like spherical harmonics or calculus of variations, you have to go hunting for explanations in some additional books. I “learnt” the spherical harmonics mostly through some online notes (esp. those by Michael Fowler of Univ. of Virginia) and QM textbooks, but I guess that a neat exposition of the topic, couched in contexts other than QM, would have been helpful. May be there is some ancient acoustics book that is really helpful. Anyway, I didn’t pursue this topic to any great depth (in fact I more or less skipped over it) because as it so happens, analytical methods fall short for anything more complex than the hydrogenic atoms.
• As to the variational calculus, avoid all the physics and maths books like a plague! Instead, learn the topic through the FEM books. Introductory FEM books have become vastly (i.e. categorically) better over the course of my generation. Today’s FEM text-books do provide a clear evidence that the authors themselves know what they are talking about! Among these books, just for learning the variational calculus aspects, I would advise going through Seshu or Fish and Belytschko first, and then through the relevant chapter from Reddy‘s book on FEM. In any case, avoid Bathe, Zienkiewicz, etc.; they are too heavily engineering-oriented, and often, in general, un-necessarily heavy-duty (though not as heavy-duty as Lancosz). Not very suitable for learning the basics of CoV as is required in the UG QM. A good supplementary book covering CoV is noted next.
• “From calculus to chaos: an introduction to dynamics” by David Acheson. A gem of a book. Small (just about 260 pages, including program listings—and just about 190 pages if you ignore them.) Excellent, even if, somehow, it does not appear on people’s lists. But if you ask me, this book is a must read for any one who has anything to do with physics or engineering. Useful chapters exist also on variational calculus and chaos. Comes with easy to understand QBasic programs (and their updated versions, ready to run on today’s computers, are available via the author’s Web site). Wish it also had chapters, say one each, on the mechanics of materials, and on fracture mechanics.
• Linear algebra. Here, keep your focus on understanding just the two concepts: (i) vector spaces, and (ii) eigen-vectors and -values. Don’t worry about other topics (like LU decomposition or the power method). If you understand these two topics right, the rest will follow “automatically,” more or less. To learn these two topics, however, don’t refer to text-books (not even those by Gilbert Strang or so). Instead, google on the online tutorials on computer games programming. This way, you will come to develop a far better (even robust) understanding of these concepts. … Yes, that’s right. One or two games programmers, I very definitely remember, actually did a much superior job of explaining these ideas (with all their complexity) than what any textbook by any university professor does. (iii) Oh yes, BTW, there is yet another concept which you should learn: “tensor product”. For this topic, I recommend Prof. Zhigang Suo‘s notes on linear algebra, available off iMechanica. These notes are a work in progress, but they are already excellent even in their present form.
• Probability. Contrary to a wide-spread impression (and to what one group of QM interpreters say), you actually don’t need much of statistics or probability in order to get the essence of QM right. Whatever you need has already been taught to you in your UG engineering/physics courses.Personally, though I haven’t yet gone through them, the two books on my radar (more from the data science angle) are: “Elementary probability” by Stirzaker, and “All of statistics” by Wasserman. But, frankly speaking, as far as QM itself is concerned, your intuitive understanding of probability as developed through your routine UG courses should be enough, IMHO.
• As to AJP type of articles, go through Dan Styer‘s paper on the nine formulations (doi:10.1119/1.1445404). But treat his paper on the common misconceptions (10.1119/1.18288) with a bit of caution; some of the ideas he lists as “misconceptions” are not necessarily so.
• arXiv tutorials/articles: Sometime after finishing quantum chemistry and before beginning quantum physics, go through the tutorial on QM by Bram Gaasbeek [^]. Neat, small, and really helpful for self-studies of QM. (It was written when the author was still a student himself.) Also, see the article on the postulates by Dorabantu [^]. Definitely helpful. Finally, let me pick up just one more arXiv article: “Entanglement isn’t just for spin” by Dan Schroeder [^]. Comes with neat visualizations, and helps demystify entanglement.
• Computational physics: Several good resources are available. One easy to recommend text-book is the one by Landau, Perez and Bordeianu. Among the online resources, the best collection I found was the one by Ian Cooper (of Univ. of Sydney) [^]. He has only MatLab scripts, not Python, but they all are very well documented (in an exemplary manner) via accompanying PDF files. It should be easy to port these programs to the Python eco-system.

Yes, we (finally) are near the end of this post, so let me add the mandatory catch-all clauses: This list is by no means comprehensive! This list supersedes any other list I may have put out in the past. This list may undergo changes in future.

Done.

OK. A couple of last minute addenda: For contrast, see the article “What is the best textbook for self-studying quantum mechanics?” which has appeared, of all places, on the Forbes!  [^]. (Looks like the QC-related hype has found its way into the business circles as well!) Also see the list at BookScrolling.com: “The best books to learn about quantum physics” [^].

OK. Now, I am really done.

A song I like:
(Marathi) “kiteedaa navyaane tulaa aaThavaave”
Music: Mandar Apte
Singer: Mandar Apte. Also, a separate female version by Arya Ambekar
Lyrics: Devayani Karve-Kothari

[Arya Ambekar’s version is great too, but somehow, I like Mandar Apte’s version better. Of course, I do often listen to both the versions. Excellent.]

[Almost 5000 More than 5,500 words! Give me a longer break for this time around, a much longer one, in fact… In the meanwhile, take care and bye until then…]

Would it happen to me, too? …Also, other interesting stories / links

1. Would it happen to me, too?

“My Grandfather Thought He Solved a Cosmic Mystery,”

reports Veronique Greenwood for The Atlantic [^] [h/t the CalTech physicist Sean Carroll’s twitter feed]. The story has the subtitle:

“His career as an eminent physicist was derailed by an obsession. Was he a genius or a crackpot?”

If you visit the URL for this story, the actual HTML page which loads into your browser has another title, similar to the one above:

“Science Is Full of Mavericks Like My Grandfather. But Was His Physics Theory Right?”

Hmmm…. I immediately got interested. After all, I do work also on foundations of quantum mechanics. … “Will it happpen to me, too?” I thought.

At this point, you should really go through Greenwood’s article, and continue reading here only after you have finished reading it.

Any one who has worked on any conceptually new approach would find something in Greenwood’s article that resonates with him.

As to me, well, right at the time that attempts were being made to find examiners for my PhD, my guide (and even I) had heard a lot of people say very similar things as Greenwood now reports: “I don’t understand what you are saying, so please excuse me.” This, when I thought that my argument should be accessible even to an undergraduate in engineering!

And now that I continue working on the foundations of QM, having developed a further, completely new (and more comprehensive) approach, naturally, Greenwood’s article got me thinking: “Would it happen to me, too? Once again? What if it does?”

…Naah, it wouldn’t happen to me—that was my conclusion. Not even if I continue talking about, you know, QM!

But why wouldn’t something similar happen to me? Especially given the fact that a good part of it has already happened to me in the past?

The reason, in essence, is simple.

I am not just a physicist—not primarily, anyway. I am primarily an engineer, a computational modeller. That’s why, things are going to work out in a different way for me.

As to my past experience: Well, I still earned my PhD degree. And with it, the most critical part of the battle is already behind me. There is a lot of resistance to your acceptance before you have a PhD. Things do become a lot easier once you have gone successfully past it. That’s another reason why things are going to work out in a different way now. … Let me explain in detail.

I mean to say, suppose that I have a brand-new approach for resolving all the essential quantum mechanical riddles. [I think I actually do!]

Suppose that I try to arrange for a seminar to be delivered by me to a few physics professors and students, say at an IIT, IISER, or so. [I actually did!]

Suppose that they don’t respond very favorably or very enthusiastically. Suppose they are outright skeptical when I say that in principle, it is possible to think of a classical mechanically functioning analog simulator which essentially exhibits all the essential quantum mechanical features. Suppose that they get stuck right at that point—may be because they honestly and sincerely believe that no classical system can ever simulate the very quantum-ness of QM. And so, short of calling me a crack-pot or so, they just directly (almost sternly) issue the warning that there are a lot of arguments against a classical system reproducing the quantum features. [That’s what has actually happened; that’s what one of the physics professors I contacted wrote back to me.]

Suppose, then, that I send an abstract to an international conference or so. [This too has actually happend, too, recently.]

Suppose that, in the near future, the conference organizers too decline my submission. [In actual reality, I still don’t know anything about the status of my submission. It was in my routine searches that I came across this conference, and noticed that I did have about 4–5 hours’ time to meet the abstracts submissions deadline. I managed to submit my abstract within time. But since then, the conference Web site has not got updated. There is no indication from the organizers as to when the acceptance or rejection of the submitted abstracts would be communicated to the authors. An enquiry email I wrote to the organizers has gone unanswered for more than a week by now. Thus, the matter is still open. But, just for the sake of the argument, suppose that they end up rejecting my abstract. Suppose that’s what actually happens.]

So what?

Since I am not a physicist “proper”, it wouldn’t affect me the way it might have, if I were to be one.

… And, that way, I could even say that I am far too smart to let something like that (I mean some deep disappointment or something like that) happen to me! … No, seriously! Let me show you how.

Suppose that the abstract I sent to an upcoming conference was written in theoretical/conceptual terms. [In actual reality, it was.]

Suppose now that it therefore gets rejected.

So what?

I would simply build a computational model based on my ideas. … Here, remember, I have already begun “talking things” about it [^]. No one has come up with a strong objection so far. (May be because they know the sort of a guy I am.)

So, if my proposed abstract gets rejected, what I would do is to simply go ahead and perform a computer simulation of a classical system of this sort (one which, in turn, simulates the QM phenomena). I might even publish a paper or two about it—putting the whole thing in purely classical terms, so that I manage to get it published. (Before doing that, I might even discuss the technical issues involved on blogs, possibly even at iMechanica!)

After such a paper (ostensibly only on the classical mechanics) gets accepted and published, I will simply write a blog post, either here or at iMechanica, noting how that system actually simulates the so-and-so quantum mechanical feature. … Then, I would perform another simulation—say using DFT. (And it is mainly for DFT that I would need help from iMechanicians or so.) After it too gets accepted and published, I will write yet another blog post, explaining how it does show some quantum mechanical-ness. … Who knows such a sequence could continue…

But such a series (of the simulations) wouldn’t be very long, either! The thing is this.

If your idea does indeed simplify certain matters, then you don’t have to argue a lot about it—people can see its truth real fast. Especially if it has to do with “hard” sciences like engineering—even physics!

If your basic idea itself isn’t so good, then, putting it in the engineering terms makes it more likely that even if you fail to get the weakness of your theory, someone else would. All in all, well and good for you.

As to the other possibility, namely, if your idea is good, but, despite putting it in the simpler terms (say in engineering or simulation terms), people still fail to see it, then, well, so long as your job (or money-making potential) itself is not endangered, then I think that it is a good policy to leave the mankind to its own follies. It is not your job to save the world, said Ayn Rand. Here, I believe her. (In fact, I believed in this insight even before I had ever run into Ayn Rand.)

As to the philosophic issues such as those involved in the foundations of QM—well, these are best tackled philosophically, not physics-wise. I wouldn’t use a physics-based argument to take a philosophic argument forward. Neither would I use a philosophical argument to take a physics-argument forward. The concerns and the methods of each are distinctly different, I have come to learn over a period of years.

Yes, you can use a physics situation as being illustrative of a philosophic point. But an illustration is not an argument; it is merely a device to make understanding easier. Similarly, you could try to invoke a philosophic point (say an epistemological point) to win a physics-based argument. But your effort would be futile. Philosophic ideas are so abstract that they can often be made to fit several different, competing, physics-related arguments. I would try to avoid both these errors.

But yes, as a matter of fact, certain issues that can only be described as philosophic ones, do happen to get involved when it comes to the area of the foundations of QM.

Now, here, given the nature of philosophy, and of its typical practitioners today (including those physicists who do dabble in philosophy), even if I become satisfied that I have resolved all the essential QM riddles, I still wouldn’t expect these philosophers to accept my ideas—not immediately anyway. In fact, as I anticipate things, philosophers, taken as a group, would never come to accept my position, I think. Such an happenstance is not necessarily to be ascribed to the personal failings of the individual philosophers (even if a lot of them actually do happen to be world-class stupid). That’s just how philosophy (as a discipline of studies) itself is like. A philosophy is a comprehensive view of existence—whether realistic or otherwise. That’s why it’s futile to expect that all of the philosophers would come to agree with you!

But yes, I would expect them to get the essence of my argument. And, many of them would, actually, get my argument, its logic—this part, I am quite sure of. But just the fact that they do understand my argument would not necessarily lead them to accept my positions, especially the idea that all the QM riddles are thereby resolved. That’s what I think.

Similarly, there also are a lot of mathematicians who dabble in the area of foundations of QM. What I said for philosophers also applies more or less equally well to them. They too would get my ideas immediately. But they too wouldn’t, therefore, come to accept my positions. Not immediately anyway. And in all probability, never ever in my lifetime or theirs.

So, there. Since I don’t expect an overwhelming acceptance of my ideas in the first place, there isn’t going to be any great disappointment either. The very expectations do differ.

Further, I must say this: I would never ever be able to rely on a purely abstract argument. That would feel like too dicey or flimsy to me. I would have to offer my arguments in terms of physically existing things, even if of a brand new kind. And, machines built out of them. At least, some working simulations. I would have to have these. I would not be able to rest on an abstract argument alone. To be satisfactory to me, I would have to actually build a machine—a soft machine—that works. And, doing just this part itself is going to be far more than enough to keep me happy. They don’t have to accept the conceptual arguments or the theory that goes with the design of such (soft) machines. It is enough that I play with my toys. And that’s another reason why I am not likely to derive a very deep sense of disenchantment or disappointment.

But if you ask me, the way I really, really like think about it is this:

If they decline my submission to the conference, I will write a paper about it, and send it, may be, to Sean Carroll or Sabine Hosenfelder or so. … The way I imagine things, he is then going to immediately translate my paper into German, add his own name to ensure its timely publication, and … . OK, you get the idea.

[In the interests of making this post completely idiot-proof, let me add: Here, in this sub-section, I was just kidding.]

2. The problem with the Many Worlds:

“Why the Many Worlds interpretation has many problems.”

Philip Ball argues in an article for the Quanta Mag [^] to the effect that many worlds means no world at all.

No, this is not exactly what he says. But what he says is clear enough that it is this conclusion which becomes inescapable.

As to what he actually says: Well, here is a passage, for instance:

“My own view is that the problems with the MWI are overwhelming—not because they show it must be wrong, but because they render it incoherent. It simply cannot be articulated meaningfully.”

In other words, Ball’s actual position is on the epistemic side, not on the ontic. However, his arguments are clear enough (and they often enough touch on issues that are fundamental enough) that the ontological implications of what he actually says, also become inescapable. OK, sometimes, the article unnecessarily takes detours into non-essentials, even into something like polemics. Still, overall, the write up is very good. Recommended very strongly.

Homework for you: If the Many Worlds idea is that bad, then explain why it might be that many otherwise reasonable people (for instance, Sean Carroll) do find the Many Worlds approach attractive. [No cheating. Think on your own and write. But if cheating is what you must do, then check out my past comment at some blog—I no longer remember where I wrote it, but probably it was on Roger Schlafly’s blog. My comment had tackled precisely this latter issue, in essential terms. Hints for your search: My comment had spoken about data structures like call-stacks and trees, and their unfolding.]

3. QM as an embarrassment to science:

“Why quantum mechanics is an “embarrassment” to science”

Brad Plumer in his brief note at the Washington Post [^] provides a link to a video by Sean Carroll.

Carroll is an effective communicator.

[Yes, he is the same one who I imagine is going to translate my article into German and… [Once again, to make this post idiot-proof: I was just kidding.]]

4. Growing younger…

I happened to take up a re-reading of David Ruelle’s book: “Chance and Chaos”. The last time I read it was in the early 1990s.

I felt younger! … May be if something strikes me while I am going through it after a gap of decades, I will come back and note it here.

5. Good introductory resources on nonlinear dynamics, catastrophe theory, and chaos theory:

If you are interested in the area of nonlinear dynamics, catastrophe theory and chaos theory, here are a few great resources:

• For a long time, the best introduction to the topic was a brief write-up by Prof. Harrison of UToronto; it still remains one of the best [^].
• Prof. Zeeman’s 1976 article for SciAm on the catastrophe theory is a classic. Prof. Zhigang Suo (of Harvard) has written a blog post of title “Recipe for catastrophe”at iMechanica [^], in which he helpfully provides a copy of Zeeman’s article. I have strongly recommended Zeeman’s write-up before, and I strongly recommend it once again. Go through it even if only to learn how to write for the layman and still not lose precision or quality.
• As to a more recent introductory expositions, do see Prof. Geoff Boeing’s blog post: “Chaos theory and the logistic map” [^]. Boeing is a professor of urban planning, and not of engineering, physics, CS, or maths. But it is he who gives the clearest idea about the distinction between randomness and chaos that I have ever run into. (However, I only later gathered that he does have a UG in CS, and a PG in Information Management.) Easy to understand. Well ordered. Overall, very highly recommended.

Apart from it all:

Happy Diwali!

A song I like:

(Hindi) “tere humsafar geet hai tere…”
Music: R. D. Burman
Singers: Kishore Kumar, Mukesh, Asha Bhosale
Lyrics: Majrooh Sultanpuri

[Has this song been lifted from some Western song? At least inspired from one?

Here are the reasons for this suspicion: (1) It has a Western-sounding tune. It doesn’t sound Indian. There is no obvious basis either in the “raag-daari,” or in the Indian folk music. (ii) There are (beautiful) changes in the chords here. But there is no concept of chords in the traditional Indian music—basically, there is no concept of harmony in it, only of melody. (iii) Presence of “yoddling” (if that’s the right word for it). That too, by a female singer. That too, in the early 1970’s! Despite all  the “taan”s and “firat”s and all that, this sort of a thing (let’s call it yoddling) has never been a part of the traditional Indian music.

Chances are good that some of the notes were (perhaps very subconsciously) inspired from a Western tune. For instance, I can faintly hear “jingle bells” in the refrain. … But the question is: is there a more direct correspondence to a Western tune, or not.

And, if it was not lifted or inspired from a Western song, then it’s nothing but a work of an absolute genius. RD anyway was one—whether this particular song was inspired from some other song, or not.

But yes, I liked this song a great deal as a school-boy. It happened to strike me once again only recently (within the last couple of weeks or so). I found that I still love it just as much, if not more.]

[As usual, may be I will come back tomorrow or so, and edit/streamline this post a bit. One update done on 2018.11.04 08:26 IST. A second update done on 2018.11.04 21:01 IST. I will now leave this post in whatever shape it is in. Got to move on to trying out a few things in Python and all. Will keep you informed, probably after Diwali. In the meanwhile, take care and bye for now…]