# Python scripts for simulating QM, part 0: A general update

My proposed paper on my new approach to QM was not accepted at the international conference where I had sent my abstract. (For context, see the post before the last, here [^] ).

“Thank God,” that’s what I actually felt when I received this piece of news, “I can now immediately proceed to procrastinate on writing the full-length paper, and also, simultaneously, un-procrastinate on writing some programs in Python.”

So far, I have written several small and simple code-snippets. All of these were for the usual (text-book) cases; all in only $1D$. Here in this post, I will mention specifically which ones…

Time-independent Schrodinger equation (TISE):

Here, I’ve implemented a couple of scripts, one for finding the eigen-vectors and -values for a particle in a box (with both zero and arbitrarily specified potentials) and another one for the quantum simple harmonic oscillator.

These were written not with the shooting method (which is the method used in the article by Rhett Allain for the Wired magazine [^]) but with the matrix method. … Yes, I have gone past the stage of writing all the numerical analysis algorithm in original, all by myself. These days, I directly use Python libraries wherever available, e.g., NumPy’s LinAlg methods. That’s why, I preferred the matrix method. … My code was not written from scratch; it was based on Cooper’s code “qp_se_matrix”, here [PDF ^]).

Time-dependent Schrodinger equation (TDSE):

Here, I tried out a couple of scripts.

The first one was more or less a straightforward porting of Ian Cooper’s program “se_fdtd” [PDF ^] from the original MatLab to Python. The second one was James Nagel’s Python program (written in 2007 (!) and hosted as a SciPy CookBook tutorial, here [^]). Both follow essentially the same scheme.

Initially, I found this scheme to be a bit odd to follow. Here is what it does.

It starts out by replacing the complex-valued Schrodinger equation with a pair of real-valued (time-dependent) equations. That was perfectly OK by me. It was their discretization which I found to be a bit peculiar. The discretization scheme here is second-order in both space and time, and yet it involves explicit time-stepping. That’s peculiar, so let me write a detailed note below (in part, for my own reference later on).

Also note: Though both Cooper and Nagel implement essentially the same method, Nagel’s program is written in Python, and so, it is easier to discuss (because the array-indexing is 0-based). For this reason, I might make a direct reference only to Nagel’s program even though it is to be understood that the same scheme is found implemented also by Cooper.

A note on the method implemented by Nagel (and also by Cooper):

What happens here is that like the usual Crank-Nicolson (CN) algorithm for the diffusion equation, this scheme too puts the half-integer time-steps to use (so as to have a second-order approximation for the first-order derivative, that of time). However, in the present scheme, the half-integer time-steps turn out to be not entirely fictitious (the way they are, in the usual CN method for the single real-valued diffusion equation). Instead, all of the half-integer instants are fully real here in the sense that they do enter the final discretized equations for the time-stepping.

The way that comes to happen is this: There are two real-valued equations to solve here, coupled to each other—one each for the real and imaginary parts. Since both the equations have to be solved at each time-step, what this method does is to take advantage of that already existing splitting of the time-step, and implements a scheme that is staggered in time. (Note, this scheme is not staggered in space, as in the usual CFD codes; it is staggered only in time.) Thus, since it is staggered and explicit, even the finite-difference quantities that are defined only at the half-integer time-steps, also get directly involved in the calculations. How precisely does that happen?

The scheme defines, allocates memory storage for, and computationally evaluates the equation for the real part, but this computation occurs only at the full-integer instants ($n = 0, 1, 2, \dots$). Similarly, this scheme also defines, allocates memory for, and computationally evaluates the equation for the imaginary part; however, this computation occurs only at the half-integer instants ($n = 1/2, 1+1/2, 2+1/2, \dots$). The particulars are as follows:

The initial condition (IC) being specified is, in general, complex-valued. The real part of this IC is set into a space-wide array defined for the instant $n$; here, $n = 0$. Then, the imaginary part of the same IC is set into a separate array which is defined nominally for a different instant: $n+1/2$. Thus, even if both parts of the IC are specified at $t = 0$, the numerical procedure treats the imaginary part as if it was set into the system only at the instant $n = 1/2$.

Given this initial set-up, the actual time-evolution proceeds as follows:

• The real-part already available at $n$ is used in evaluating the “future” imaginary part—the one at $n+1/2$
• The imaginary part thus found at $n+1/2$ is used, in turn, for evaluating the “future” real part—the one at $n+1$.

At this point that you are allowed to say: lather, wash, repeat… Figure out exactly how. In particular, notice how the simulation must proceed in integer number of pairs of computational steps; how the imaginary part is only nominally (i.e. only computationally) distant in time from its corresponding real part.

Thus, overall, the discretization of the space part is pretty straight-forward here: the second-order derivative (the Laplacian) is replaced by the usual second-order finite difference approximation. However, for the time-part, what this scheme does is both similar to, and different from, the usual Crank-Nicolson scheme.

Like the CN scheme, the present scheme also uses the half-integer time-levels, and thus manages to become a second-order scheme for the time-axis too (not just space), even if the actual time interval for each time-step remains, exactly as in the CN, only $\Delta t$, not $2\Delta t$.

However, unlike the CN scheme, this scheme still remains explicit. That’s right. No matrix equation is being solved at any time-step. You just zip through the update equations.

Naturally, the zipping through comes with a “cost”: The very scheme itself comes equipped with a stability criterion; it is not unconditionally stable (the way CN is). In fact, the stability criterion now refers to half of the time-interval, not full, and thus, it is a bit even more restrictive as to how big the time-step ($\Delta t$) can be given a certain granularity of the space-discretization ($\Delta x$). … I don’t know, but guess that this is how they handle the first-order time derivatives in the FDTD method (finite difference time domain). May be the physics of their problems itself is such that they can get away with coarser grids without being physically too inaccurate, who knows…

Other aspects of the codes by Nagel and Cooper:

For the initial condition, both Cooper and Nagel begin with a “pulse” of a cosine function that is modulated to have the envelop of the Gaussian. In both their codes, the pulse is placed in the middle, and they both terminate the simulation when it reaches an end of the finite domain. I didn’t like this aspect of an arbitrary termination of the simulation.

However, I am still learning the ropes for numerically handling the complex-valued Schrodinger equation. In any case, I am not sure if I’ve got good enough a handle on the FDTD-like aspects of it. In particular, as of now, I am left wondering:

What if I have a second-order scheme for the first-order derivative of time, but if it comes with only fictitious half-integer time-steps (the way it does, in the usual Crank-Nicolson method for the real-valued diffusion equation)? In other words: What if I continue to have a second-order scheme for time, and yet, my scheme does not use leap-frogging? In still other words: What if I define both the real and imaginary parts at the same integer time-steps $n = 0, 1, 2, 3, \dots$ so that, in both cases, their values at the instant $n$ are directly fed into both their values at $n+1$?

In a way, this scheme seems simpler, in that no leap-frogging is involved. However, notice that it would also be an implicit scheme. I would have to solve two matrix-equations at each time-step. But then, I could perhaps get away with a larger time-step than what Nagel or Cooper use. What do you think? Is checker-board patterning (the main reason why we at all use staggered grids in CFD) an issue here—in time evolution? But isn’t the unconditional stability too good to leave aside without even trying? And isn’t the time-axis just one-way (unlike the space-axis that has BCs at both ends)? … I don’t know…

PBCs and ABCs:

Even as I was (and am) still grappling with the above-mentioned issue, I also wanted to make some immediate progress on the front of not having to terminate the simulation (once the pulse reached one of the ends of the domain).

So, instead of working right from the beginning with a (literally) complex Schrodinger equation, I decided to first model the simple (real-valued) diffusion equation, and to implement the PBCs (periodic boundary conditions) for it. I did.

My code seems to work, because the integral of the dependent variable (i.e., the total quantity of the diffusing quantity present in the entire domain—one with the topology of a ring) does seem to stay constant—as is promised by the Crank-Nicolson scheme. The integral stays “numerically the same” (within a small tolerance) even if obviously, there are now fluxes at both the ends. (An initial condition of a symmetrical saw-tooth profile defined between $y = 0.0$ and $y = 1.0$, does come to asymptotically approach the horizontal straight-line at $y = 0.5$. That is what happens at run-time, so obviously, the scheme seems to handle the fluxes right.)

Anyway, I don’t always write everything from the scratch; I am a great believer in lifting codes already written by others (with attribution, of course :)). Thus, while thus searching on the ‘net for some already existing resources on numerically modeling the Schrodinger equation (preferably with code!), I also ran into some papers on the simulation of SE using ABCs (i.e., the absorbing boundary conditions). I was not sure, however, if I should implement the ABCs immediately…

As of today, I think that I am going to try and graduate from the transient diffusion equation (with the CN scheme and PBCs), to a trial of the implicit TDSE without leap-frogging, as outlined above. The only question is whether I should throw in the PBCs to go with that or the ABCs. Or, may be, neither, and just keep pinning the  $\Psi$ values for the end- and ghost-nodes down to $0$, thereby effectively putting the entire simulation inside an infinite box?

At this point of time, I am tempted to try out the last. Thus, I think that I would rather first explore the staggering vs. non-staggering issue for a pulse in an infinite box, and understand it better, before proceeding to implement either the PBCs or the ABCs. Of course, I still have to think more about it… But hey, as I said, I am now in a mood of implementing, not of contemplating.

Why not upload the programs right away?

BTW, all these programs (TISE with matrix method, TDSE on the lines of Nagel/Cooper’s codes, transient DE with PBCs, etc.) are still in a fluid state, and so, I am not going to post them immediately here (though over a period of time, I sure would).

The reason for not posting the code runs something like this: Sometimes, I use the Python range objects for indexing. (I saw this goodie in Nagel’s code.) At other times, I don’t. But even when I don’t use the range objects, I anyway am tempted to revise the code so as to have them (for a better run-time efficiency).

Similarly, for the CN method, when it comes to solving the matrix equation at each time-step, I am still not using the TDMA (the Thomas algorithm) or even just sparse matrices. Instead, right now, I am allocating the entire $N \times N$ sized matrices, and am directly using NumPy’s LinAlg’s solve() function on these biggies. No, the computational load doesn’t show up; after all, I anyway have to use a 0.1 second pause in between the rendering passes, and the biggest matrices I tried were only $1001 \times 1001$ in size. (Remember, this is just a $1D$ simulation.) Even then, I am tempted a bit to improve the efficiency. For these and similar reasons, some or the other tweaking is still going on in all the programs. That’s why, I won’t be uploading them right away.

Anything else about my new approach, like delivering a seminar or so? Any news from the Indian physicists?

I had already contacted a couple of physics professors from India, both from Pune: one, about 1.5 years ago, and another, within the last 6 months. Both these times, I offered to become a co-guide for some computational physics projects to be done by their PG/UG students or so. Both times (what else?) there was absolutely no reply to my emails. … If they were to respond, we could have together progressed further on simulating my approach. … I have always been “open” about it.

The above-mentioned experience is precisely similar to how there have been no replies when I wrote to some other professors of physics, i.e., when I offered to conduct a seminar (covering my new approach) in their departments. Particularly, from the young IISER Pune professor whom I had written. … Oh yes, BTW, there has been one more physicist who I contacted recently for a seminar (within the last month). Once again, there has been no reply. (This professor is known to enjoy hospitality abroad as an Indian, and also use my taxpayer’s money for research while in India.)

No, the issue is not whether the emails I write using my Yahoo! account go into their span folder—or something like that. That would be too innocuous a cause, and too easy to deal with—every one has a mobile-phone these days. But I know these (Indian) physicists. Their behaviour remains exactly the same even if I write my emails using a respectable academic email ID (my employers’, complete with a .edu domain). This was my experience in 2016, and it repeated again in 2017.

The bottom-line is this: If you are an engineer and if you write to these Indian physicists, there is almost a guarantee that your emails will go into a black-hole. They will not reply to you even if you yourself have a PhD, and are a Full Professor of engineering (even if only on an ad-hoc basis), and have studied and worked abroad, and even if your blog is followed internationally. So long as you are engineer, and mention QM, the Indian physicists simply shut themselves off.

However, there is a trick to get them to reply you. Their behavior does temporarily change when you put some impressive guy in your cc-field (e.g., some professor friend of yours from some IIT). In this case, they sometimes do reply your first email. However, soon after that initial shaking of hands, they somehow go back to their true core; they shut themselves off.

And this is what invariably happens with all of them—no matter what other Indian bloggers might have led you to believe.

There must be some systemic reasons for such behavior, you say? Here, I will offer a couple of relevant observations.

Systemically speaking, Indian physicists, taken as a group (and leaving any possible rarest of the rare exceptions aside), all fall into one band: (i) The first commonality is that they all are government employees. (ii) The second commonality they all tend to be leftists (or, heavily leftists). (iii) The third commonality is they (by and large) share is that they had lower (or far lower) competitive scores in the entrance examinations at the gateway points like XII, GATE/JAM, etc.

The first factor typically means that they know that no one is going to ask them why they didn’t reply (even to people like with my background). The second factor typically means that they don’t want to give you any mileage, not even just a plain academic respect, if you are not already one of “them”. The third factor typically means that they simply don’t have the very intellectual means to understand or judge anything you say if it is original—i.e., if it is not based on some work of someone from abroad. In plain words: they are incompetent. (That in part is the reason whenever I run into a competent Indian physicist, it is both a surprise and a pleasure. To drop a couple of names: Prof. Kanhere (now retired) from UoP (now SPPU), and Prof. Waghmare of JNCASR. … But leaving aside this minuscule minority, and coming to the rest of the herd: the less said, the better.)

In short, Indian physicists all fall into a band. And they all are very classical—no tunneling is possible. Not with these Indian physicists. (The trends, I guess, are similar all over the world. Yet, I definitely can say that Indians are worse, far worse, than people from the advanced, Western, countries.)

Anyway, as far as the path through the simulations goes, since no help is going to come from these government servants (regarded as physicists by foreigners), I now realized that I have to get going about it—simulations for my new approach—entirely on my own. If necessary, from the basic of the basics. … And that’s how I got going with these programs.

Are these programs going to provide a peek into my new approach?

No, none of these programs I talked about in this post is going to be very directly helpful for simulations related to my new approach. The programs I wrote thus far are all very, very standard (simplest UG text-book level) stuff. If resolving QM riddles were that easy, any number of people would have done it already.

… So, the programs I wrote over the last couple of weeks are nothing but just a beginning. I have to cover a lot of distance. It may take months, perhaps even a year or so. But I intend to keep working at it. At least in an off and on manner. I have no choice.

And, at least currently, I am going about it at a fairly good speed.

For the same reason, expect no further blogging for another 2–3 weeks or so.

But one thing is for certain. As soon as my paper on my new approach (to be written after running the simulations) gets written, I am going to quit QM. The field does not hold any further interest to me.

Coming to you: If you still wish to know more about my new approach before the paper gets written, then you convince these Indian professors of physics to arrange for my seminar. Or, else…

… What else? Simple! You. Just. Wait.

[Or see me in person if you would be visiting India. As I said, I have always been “open” from my side, and I continue to remain so.]

A song I like:
(Hindi) “bheegee bheegee fizaa…”
Music: Hemant Kumar
Singer: Asha Bhosale
Lyrics: Kaifi Aazmi

History:
Originally published: 2018.11.26 18:12 IST
Extension and revision: 2018.11.27 19.29 IST

# General update: Will be away from blogging for a while

I won’t come back for some 2–3 weeks or more. The reason is this.

As you know, I had started writing some notes on FVM. I would then convert my earlier, simple, CFD code snippets, from FDM to FVM. Then, I would pursue modeling Schrodinger’s equation using FVM. That was the plan.

But before getting to the nitty-gritties of FVM itself, I thought of jotting down a note, once and for all, putting in writing my thoughts thus far on the concept of flux.

If you remember, it was several years ago that I had mentioned on this blog that I had sort of succeeded in deriving the Navier-Stokes equation in the Eulerian but differential form (d + E for short).

… Not an achievement by any stretch of imagination—there are tomes written on say, differentiable manifolds and whatnot. I feel sure that deriving the NS equations in the (d + E) form would be less than peanuts for them.

Yet, the fact of the matter is: They actually don’t do that!

Show me a single textbook or a paper that does that. If not at the UG level, then at least at the PG level, but one that is written using the language of only plain calculus, as used by engineers—not that of advanced analysis.

And as to the UG/PG books from engineering:

What people normally do is to derive these equations in its integral form, whether using the Lagrangian or the Eulerian approach. That is, they adopt either the (i + L) approach or the (i + D) approach.

At some rare times, if they at all begin fluid dynamics with a differential form of the NS equations, then they invariably follow the Lagrangian approach, never the Eulerian. That is, they invariably begin with only (d + L)—even in those cases when their objective is to obtain (d + E). Then, after having derived (d +L) , they simply invoke some arbitrary-looking vector calculus identities to “transform” those equations from (d + L) to (d +E).

And, worse:

They never discuss the context, meaning, or proofs of those identities. None from fluid dynamics or CFD side does that. And neither do the books on maths written for scientists and engineers.

The physical bases of the “transformation” process must remain a mystery.

When I started working through it a few years ago, I realized that the one probable reason why they don’t use the (d +E) form right from the beginning is because: forget the NS equations, no one understands even the much simpler idea of the flux—if it is to be couched entirely in the settings of (d+E). You see, the idea of the flux too always remains couched in the integral form, never the differential. For example, see Narasimhan [^]. Or, any other continuum mechanics books that impresses you.

It’s no accident that the Wiki article on Flux [^] says that it

needs attention from an expert in Physics.

And then, more important for us, the text of the article itself admits that the formula it notes, for a definition of flux in differential terms, is

an abuse of notation

See the section here [^].

Also, ask yourself, why is a formula that is free of the abuse of notation not being made available? In spite of all those tomes having been written on higher mathematics?

Further, there were also other related things I wanted to write about, like an easy pathway to the idea of tensors in general, and to that of the stress tensor in particular.

So, I thought of writing it down it for once and for all, in one note. I possibly could convert some parts of it into a paper later on, perhaps. For the time being though, the note would be more in the nature of a tutorial.

I started writing down the note, I guess, from 17 August 2018. However, it kept on growing, and with growth came reorganization of material for a better hierarchy or presentation. It has already gone through some 4–5 thorough re-orgs (meaning: discarding the earlier LaTeX file entirely and starting completely afresh), and it has already become more than 10 LaTeX pages. Even then, I am nowhere near finishing it. I may be just about half-way through—even though I have been working on it for some 7–8 hours every day for the past fortnight.

Yes, writing something in original is a lot of hard work. I mean “original” not in the sense of discovery, but in the sense of a lack of any directly citable material whatsoever, on the topic. Forget copy-pasting. You can’t even just gather a gist of the issue so that you could cite it.

And, the trouble here is, this topic is otherwise so very mature. (It is some 150+ years old.) So, you know that if you go even partly wrong, the whole world is going to pile on you.

And that way, in my experience, when you write originally, there is at least 5–10 pages of material you typically end up throwing away for every page that makes it to the final, published, version. Yes, the garbage thrown out is some 5–10 times the material retained in—no matter how “simple” and “straightforward” the published material might look.

Indeed, I could even make a case that the simpler and the more straight-forward the published material looks, if it also happens to be original, then the more strenuous it has been, on the part of the author.

Few come to grasp this simple an observation, ever, in their entire life.

As a case in point, I wish to recall here my conference paper on diffusion. [To be added here soon enough.]

I have many times silently watched people as they were going through this paper for the first time.

Typically, when engineers read it, they invariably come out with a mild expression which suggests that they probably were thinking of something like: “isn’t it all so simple and straight-forward?” Sometimes they even explicitly ask: “And, what do you say was the new contribution here?” [Even after having gone through both the abstract and the conclusion part of it, that is.]

On the other hand, on the four-five rare occasions when I have had the opportunity to watch professional mathematicians go through this paper of mine, in each case, the expression they invariably gave at the end of finishing it was as if they still were very intently absorbed in it. In particular, they never do ask me what was new about it—they just remain deeply engaged in what looks like an exercise in “fault-finding”, i.e., in checking if any proof, theorem or lemma they had ever had come across could be used in order to demolish the new idea that has been presented. Invariably, they give the same argument by way of an objection. Invariably, I explain why their argument does not address the issue I have raised in the paper. Invariably they chuckle and then go back to the paper and to their intent thinking mode, to see if there is any other weakness to my basic argument…

Till date (even after more than a decade), they haven’t come back.

But in all cases, they were very ready to admit that they were coming across this argument for the first time. I didn’t have to explain it to them that though the language and the tone of the paper looked simple enough, the argument itself was not easy to derive originally.

No, the notes which I am currently working on are nowhere near as original as that. [But yes, original, these are.]

Yet, let me confess, even as I keep prodding through it for the better part of the day the way I have done over the past fortnight or so, I find myself dealing with a certain doubt: wouldn’t they just dismiss it all as being too obvious? as if all the time and effort I spent on it was, more or less, ill spent? that it was all meaningless to begin with?

Anyway, I want to finish this task before resuming blogging—simply because I’ve got a groove about it by now… I am in a complete and pure state of anti-procrastination.

… Well, as they say: Make the hay while the Sun shines…

A Song I Like:
(Marathi) “dnyaandev baaL maajhaa…”
Singer: Asha Bhosale
Lyrics: P. Savalaram
Music: Vasant Prabhu

# “Blog” less; write journal papers!

“‘Blog’ less; write journal papers.”

That’s my NYR for 2018.

Allow me to explain.

My research is not experimental, neither is it connected with, say, design of a new machine or development of a new manufacturing process. The most concrete aspect my work involves only computational modeling. But that too is not of the kind which engineering researchers typically undertake. I don’t do FEM of this multi-physics problem or that. What I work on are some very fundamental issues of physics and engineering.

My research thus is decidedly theoretical, often bordering on being “speculative.” It tends to concentrate on fundamental aspects. For decades by now, I have been trying to tackle some of the trickiest, deepest or very abstract problems (e.g. foundations of QM). At other times, I have been busy just isolating something new as a problem in its right (e.g., instantaneous action-at-a-distance in diffusion, or non-uniqueness of solution to the diffusion equation, or the fundamentality of stress vis-a-vis strain, or mode transitions in ideal vibrations and their relation to vibrations in the real mechanical system, or the physical meaning of the delta of calculus of variations….).

OK, there are some simple experiments here and there I might do. But they are not a very significant aspect of my work. The experiments are more in the nature of illustrations (e.g. melting snowman). They are not even fully in the nature of quantitative validations, let alone the prime vehicles to discovery. So, they are just “potatoes” of my research. The meat is: deep theoretical issues themselves. That’s what it’s like when you say “fundamental.”

The only way in which you can formulate or tackle such problems—fundamental or foundational—is by being a bit “relaxed” about both the specifics of your topic and the way you go about tackling it.

If you believed too much in the existing theory, you wouldn’t be able to spot unidentified problems with it or find new solutions to the known ones. If you try to do theoretical research and if you still try to stick to a schedule like what they do in experimental research (say in designing and fabricating a gadget, complete with bill of materials, or in developing a process, complete with prototype 1, prototype 2, etc.), you wouldn’t able to even get off to a decent start. After all, a schedule can be made from only those ingredients that are already known to you, not of never seen possibilities or unknown ideas. And, while in experimental research, reality has a wonderful way to throw up new possibilities, you have no such luxury in theoretical research. Every “never seen” possibility has to be forged by your own mind. If you don’t think in a relaxed manner, you are never going to believe that the issue is easy enough for you to tackle it.

But one unintended consequence of it all is that, in theoretical research like mine, it’s easy (far too easy in fact) to get a bit too relaxed. It is easy to pursue too many diverse theoretical threads, and in examining them, to run around in circles and so keep on getting back to the same points again and again.

But now I have come to realize that perhaps time has come to stop pursuing new threads in my research and to consolidate what has already been learnt.

The best way I can think of for doing the latter is: writing papers.

In particular, I have to kick aside this one habit: writing things down only when and as “inspiration” strikes.

Writing thoughts down (maintaining pocket diaries) has done a world of good to me. But this long-pursued activity seems to have by now come, in my case, to the point of diminishing marginal utility.

In place of this habit (of keeping on idly brain-storming and noting down possibilities it throws up) I would now like to put in place another habit: writing things (papers, actually) down in a structured, routine, regular, day-to-day, and time-bound manner. Allow me to explain this part too.

Given the way I have pursued my research (and in fact, given even the very nature of problems I ended up tackling), it would have been impossible for me to say something like this:

“OK! January, diffusion paper! February, stress-strain paper! March and April, QM position paper!”

“… What, in February, I don’t write something on QM? neither on diffusion? How ridiculous?”

That is how I would have reacted. But not any more.

Instead, I am now going to be a bit “bureaucratic” about my research. (UGC and AICTE folks ought to be happy in discovering a new soul-mate in me!)

What I am going to do is what I indicated just minutes ago. I am going to make some kind of a “time-table”: this period, work (i.e. actually write papers about) only this particular problem. Leave aside all other issues. Just finish that particular paper. Only then move to those other, more interesting (even alluring) issues in a next delimited period specifically allocated for that. I will have to pursue this policy. And I had better.

After all, while “passively” letting myself jump from issues to issues has yielded a lot of new insights, there are any number of issues where I have “hit the plateau” by now—and I mean those words in a positive sense. By “hitting the plateau,” I mean not a loss of creativity or originality, but a sense, even a firm realization (based on logic) that a certain stage of completeness is already achieved.

And that’s why, I am going to concentrate on “professionally” writing papers, in the next year. Following some kind of a time-bound schedule. As if I were writing a report, or delivering a software product on its schedule. So, it’s high time I became a bit less “creative” and more “professional,” to put it vaguely.

Since I will not be pursuing this bit of this idea or that bit of that idea a lot, I will be blogging less. And since a lot of my research seems to have actually “hit the plateau” in the above-mentioned, positive sense, I would instead be writing papers.

Hence the “slogan”: “`Blog’ less, write journal papers!”

That’s my NYR for 2018…. though I wouldn’t wait for 2018 to arrive before getting going on it. After all, a new year is just an excuse to make resolutions. The digits in the date aren’t important. A definite, demarcated change (“quantum jump” if you will! [LOL!]) is. But a change of the last digit in the YYYY, since it comes only after as long a period as one complete year, is a good time to making the required definite change.

So, there. I will keep you posted, with very brief notes here and there, as to how this paper-writing “business” is actually progressing in my case. My immediate plan is to get going writing the diffusion papers, and to finish writing them, right in January 2018.

Let’s see how things actually progress.

A Song I Like:

This is that Marathi song which I said I had liked a lot during my childhood vacation (see my last 2–3 posts). I still like it. It is the one which has a decidedly Western touch, but without spoiling or compromising on the Indian sense of melody. …

(Marathi) “raajaa saarangaa, maajyaa saarangaa”
Music: Hridaynath Mangeshkar
Singer: Lata Mangeshkar
Lyrics: Shanta Shelke

Bye for now, make a time-table you can stick to, and also take care to execute on it. … Best wishes for a happy and prosperous new year!