John R. Rice's "Future Scientific Software Systems"

Today I stumbled upon the slides of a presentation, "Future Scientific Software Systems", presented at the first IEEE Computer Society Workshop on Computational Science and Engineering, in October 1996 at Purdue University.  The presenter was John R. Rice (1934-2024), a distinguished professor of computer science at Purdue.

Some remarkable statements in the talk did not make it into the published version, which appeared the following year in the journal, IEEE Computational Science and Engineering. Slide 20, for example, states that Numerical Recipes is "Extremely successful commercially and a failure scientifically.  The lessons learned in the 1950s-1970s are ignored."  Under the bullet for Microsoft Excel, it simply says "Ditto".  Late in the slide, it says "Poor, unreliable, inefficient software can be a commercial success.  Most users have no reliable way to assess software quality."

Much has been written about the flaws of Microsoft Excel elsewhere, so I suspect there won't be much controversy over this statement.  Rice's claim about Numerical Recipes was a shock to my system when I first heard it (from Rice himself, though at a different forum). The first edition of NR (dating to 1986) was the bread and butter of the computational component of my undergraduate research project in 1994-1995; admittedly I had not had any formal training in computer science beyond high school at the time I was working on that project.  However, my elders seemed to treat NR as an authoritative source, and I had no reason to think otherwise until I heard Prof. Rice denounce it just a year or two later.  The fact that NR was written by users (albeit very sophisticated ones) rather than "professional" computer scientists or computational mathematicians, lent some plausibility to the claim.

About a decade later, when I was a working professional, I discovered empirically an example of one of NR's flaws.  I was trying to understand spectral analysis of unevenly spaced data, and NR promotes the Lomb-Scargle method.  I quickly discovered that this was a very active field of research, and there was a plethora of alternatives to Lomb-Scargle, though I didn't have the time to investigate them all and figure out which ones were the most fit for purpose for our work.  No hint of this rich literature is made in NR, not even in the third (2007) edition.  I had finally discovered evidence that what Rice had said a decade earlier might not just be the opinion of a disgruntled academic.  On the other hand, the community of "professionals" could be considered as having equally failed to provide a pragmatic alternative to NR to users.  

Returning to Rice's presentation, the slides after slide 20 start to look ahead in time.  The final slide (slide 33) is amusing as it is titled "High impact applications that won't happen". He predicts that "By 2015 we will not have simulations that are reliable and accurate for"

• Weather forecasts of several days
• Social interactions such as the economy, small groups, warfare, business growth
• Life forms of a single cell
• Geophysics such as earthquakes and volcano eruptions
• Climate
• Software engineering of large Fortran/C applications 

Here we are ten years after 2015, in 2025.  I think the list of applications that did not happen remains accurate, except possibly for the first bullet (weather forecasts).  He does not define how "reliable and accurate" such forecasts need to be, but I think it would be unfair to claim that we cannot make such forecasts reasonably well in 2025.

Rice looked ahead 20 years.  Could we see progress on the rest of the above list in the next twenty?  One thing Rice did not foresee is that simulations might come to be supplanted by artificial intelligence methods, such as large language models. We have already started to see AI encroach as a competitor to simulations in weather forecasting.  Alternatively, simulations and AI might find a way to work together to make progress on these kinds of problems.  I'm reluctant to be completely skeptical that no progress on the above list will be made in the next 20 years, though not necessarily by simulations alone.

 

Reference

J. R. Rice, 1997:  Future scientific software systems.  IEEE Computational Science and Engineering, April/June issue, pp. 44-48. 

Peter Lax, 1926-2025

As I noted just last month, DTLR does not dwell on mathematics very much, but a second exception seems just as warranted as my earlier post last month.  Today we learn of the passing of Abel Prize laureate Peter D. Lax (1926-2025), a retired professor at the Courant Institute at NYU, last Friday.  He was a highly accomplished pure and applied mathematician, who worked in the field of partial differential equations, and on numerical methods for their solution.  Much of this work has direct relevance to applied physics and engineering, including fluid dynamics.

Prof. Lax is also the only Abel Prize winner I have ever met in person.  It was just a brief meeting during a visit I made to the Courant Institute in the late 2000's on other business.  We did not exchange many words, but I was honored to meet him.

I've attended lectures by at least two other Abel Prize winners, S.R.S. Varadhan and the late John Nash, but did not meet them face to face.

I'll take this chance to mention my encounters with winners of the other major international prizes in mathematics.  As far as I know, I have neither met nor attended lectures by any of the Fields Medalists, except for Shing-Tung Yau.  As for the Wolf Prize in Mathematics, both Lax and Yau are the only ones I've personally encountered as noted.  Well, it must be evident that I don't attend math conferences or math lectures very often.

Bad philosophy or just an urge for glory?

I haven't read many of Carlo Rovelli's works, but I did enjoy an essay he published this month in Nature.  "There is a healthy sense of crisis in fundamental physics" he says, but he is dismayed by commonly seen demands for physics "beyond" the standard model, conventional quantum theory, and general relativity.  He thinks this is because of "bad philosophy" or mis-readings of philosophers of science such as Kuhn and Popper, who he claims are understood to endorse radical "overthrows" of existing theories and treating all speculative theories equally seriously until they've been falsified.  He argues that previous "paradigm shifts" are actually more "conservative" than commonly understood, and that despite this, radical new theories are driven primarily by confronting data not currently understood, as well as apparent contradictions among different pieces of knowledge.

It is a good and thought-provoking essay.  I basically agree with his point that the history of science is far more methodologically valuable to study than philosophy of science.  However I surmise that he's overthought the explanation for the craze for "physics beyond the standard model" (such as supersymmetry and string theory).  I think the more radical alternatives just have greater potential for scientific glory than more "conservative" approaches (such as loop quantum gravity, a field he worked in and seems to think is a "proof of concept" for an approach more closely tied to existing quantum theory and gravitational theories, though he points out some of its "radical" features).  Thus, the appeal of following the wilder approaches is the chance to attain heroic status.

I haven't summarized Rovelli's essay very eloquently; he writes very well and I recommend reading it.  Even though his should not be the final word (I'm certainly not convinced that the wilder speculative theories should be completely abandoned) but it's a good counterpoint to much of what we read, especially in accounts of popular physics.

Mathematical fluid dynamics

I don't often write about mathematics here on DTLR, but this piece by Jack Murtagh in Scientific American is a worthy exception, as it pertains to fluid dynamics.  Namely it reports on the work of 3 mathematicians who claim to have found a way to derive the hierarchy of methods for 3 levels of describing fluid motion, the individual particle level, the statistical description of particle behavior of Maxwell and Boltzmann, and the continuum level of the Euler and Navier-Stokes equations.  The mathematicians posted their work to arXiv, so let the peer review proceed.

 

 

 

Thoughts on graduate education in physics

The American Journal of Physics this month features an article by Laurie McNeil, who was given a teaching award named after J. D. Jackson.  The article is based on her acceptance speech for the award, in which she mentions never having taken a full grad course based on Jackson's classic (and notorious) electromagnetism textbook.  But the important point about her article is that it offers a perspective on graduate physics education that focuses not on reproducing physics professors, but on providing success skills for its graduates, most of whom do not make permanent careers in academia.  She does something that few physics departments bother to do - she tried to track down the current positions of all her department's alumni since 1981.  Most physicists only keep track of their graduates who go on to research/academic careers, as she notes, and deem the rest "lost to the profession".

I fully agree with the article.  I should note, though, that the article is a high-level, vision type piece, without a lot of nuts and bolts.  For example, I would advocate that graduate education in physics should include an allowance, if not strong encouragement, for students to take a break to do a summer internship or two in industry, government, or the nonprofit sector.  This is something I've previously discussed on this blog.  If I sat down and thought hard about it, I would add additional recommendations.  The currently common form of physics graduate education is in my view ritualistic and inadequate for purpose.

 

 

The International Year of Quantum Science and Technology

UNESCO has declared that this year, 2025, is the International Year of Quantum Science and Technology. Numerous international and national physics associations have endorsed the theme.  There are a few industry and academic partners as well.  DTLR welcomes the themed year, just as I did for 2015's "International Year of Light and Light-based Technologies". 

2025 is an appropriate year to celebrate all things quantum.  Though quantum physics got its real start in 1900 with Max Planck's blackbody radiation theory, the theoretical developments from then through 1925 are now characterized as the "old quantum theory".  Quantum theory began to assume the form that we now understand it as in 1925, one century ago, with the publication of Schrodinger's wave mechanics, Heisenberg's matrix mechanics, and Pauli's exclusion principle.

Hence let us welcome the new year of 2025 and take this opportunity to celebrate a century of quantum physics.