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"This is an unexpected phenomenon, contrary to what we thought we knew about nanopore transport. It took three years to work out what it was the simulations wer
by ahelwer 8y ago
"This is an unexpected phenomenon, contrary to what we thought we knew about nanopore transport. It took three years to work out what it was the simulations were showing us. After exploring many potential solutions, the breakthrough came when we realized that we should not assume water is incompressible. Now that we understand what's happening in the computer simulations, we are able to reproduce this phenomenon in theoretical calculations."
Pretty interesting that the rules of the simulation were such that it took three years to explain an emergent phenomenon. I probably would have written it off as floating point shenanigans.
- slimsag 8y ago> I probably would have written it off as floating point shenanigans. Pretty sure anyone in actual scientific computing stays far, far away from floating point numbers and sticks to arbitrary precision ones instead.
- philipkglass 8y agoDouble precision floating point numbers are the most common in scientific computing. Sometimes software will mix in single precision for more speed. The molecular dynamics package GROMACS can use mixed single/double precision, for example: http://manual.gromacs.org/documentation/2016/install-guide/index.html http://manual.gromacs.org/documentation/2016/install-guide/i...
- wtallis 8y agoI'm quite curious what niche of scientific computing you've dealt with that gave you that impression.
- geoalchimista 8y agoError analysis can tell you whether the roundoff error would grow exponentially with time steps for the algorithm you use. If you avoid this kind of conditions in scientific computing, then double precision floating point number would work perfectly fine. And by the way, scientific computing is a diverse discipline, or a federation of disciplines. Say, there may be little overlap between the algorithms, tools, and environments that a physicist uses for scientific computing and those used by a bioinformatician. So I don't think it is universally true in scientific computing that arbitrary precision is preferable to double precision.
- leovailati 8y agoI found it counterintuitive that it was predicted in simulation and then confirmed with experiments, rather than the other way around.
- felipemnoa 8y agoThis is how lots of new science is discovered. One example is the Theory of Relativity. Sure it was not a software simulation per se but it was still simulated on paper. It was not confirmed by experiment until several years latter.
- wallace_f 8y agoIf I'm not mistaken I think what OP is referring to is an apparent emergent phenomena that contradicted theory, whereas I believe GR was discovered through theory (ie Einstein's "happiest thought" was in an elevator shaft).
- waiquoo 8y agoWith nanopore experiments, it's not unusual to see unusual behavior that could possibly be explained by macromolecule interactions, contamination, or electrical interference. There are often more unknowns than knowns due to the scale of the system, novel materials, measurement precision, unique physics, etc etc... What usually happens is the undesired measurement gets filed away for 'later study'. Unless you have some theoretical framework or model that explains it, you may never go back. So probably the experimental part was previously dismissed as an error or contamination, but now it is interesting because there is an explanation
- ahartmetz 8y agoThat is how it's supposed to work. You build a theory based on known phenomena, then you look for any new phenoma it predicts and test them in experiments. Without the new phenomena, your theory is more likely to be just "overfitting" the data. A famous example of overfitting the data - adding more cruft to account for unexplained aspects - is this: https://en.wikipedia.org/wiki/Deferent_and_epicycle https://en.wikipedia.org/wiki/Deferent_and_epicycle
- 8y ago