splittingTimes
Joined 10 years ago
Joined 10 years ago
https://www.australiangeographic.com.au/news/2024/08/high-co...
You would start from a Lagrangian formulation of the classical interaction, let's say Light-Matter, that would yield for example the Schrodinger and Maxwell equations. Following a Legendre transformation (there a post on the HN front page the other day on that) you end up with a so-called Hamilton operator from which you can derive a (huge) set of coupled differential equations which you then solve.
Here, if you wanted to increase temporal accuracy, it typically leads simply to longer calculation times.
We also tried a different approach using Feynman's path integrals and boy did that explode numerically. We optimized our programs to the point where everything was reduced to work on bits, but to no avail it was numerically unstable and the memory consumption when through the roof the longer or more accurate you wanted to make the simulation.
So, I would argue that NO, Feynman does not make it easier per se.
However, other groups made it work somehow.
As a starting point you can check that paper and it's references from the introduction section.
Did manage to make it work with AsciiDoclet?
Where did you get the info from that it was on Israel's request and not of their own accord?
What do the all do all day??
For publishing documentation / to build the web site: Antora [2].
AsciiDoc has a bit more features compared to Markdown which allows for a richer and more pleasant presentation of the docs.
Antora allows you to have the project documentation in the actual project repositories. It then pulls the docs from all the different repos together to build the site. This also allows you to have the released product versions go in-synch with the docs versions. Antora builds each version of the product as part of one site. The reader can explore different product versions or navigate between pages across versions.
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(1) Hue was not a good dimension for encoding magnitude information, i.e. rainbow color maps are bad.
(2) The mechanisms in human vision responsible for high spatial frequency information processing are luminance channels. If the data to be represented have high spatial frequency, use a color map which has a strong luminance variation across the data range.
(3) For interval and ratio data, both luminance- and saturation-varying color maps should produce the effect of having equal steps in data value correspond to equal perceptual steps, but the first will be most effective for high spatial frequency data variations and the second will be most effective for low spatial frequency variations.
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[1] the original link got removed from IBMs website. Back in the day it was under
https://www.research.ibm.com/people/l/lloydt/color/color.HTM
A pdf copy is here:
https://github.com/frankMilde/interesting-reads/blob/master/...
Not sure why they stopped it some years ago. Real pitty.
You might wanna skip that postdoc thing though if you know you don't wanna stay in academia (and trust me, you don't).
This experience drove me right into theoretical physics and writing computer simulations of electron dynamics and light-matter interactions in confined semiconductors (quantum dots, graphene and the like). That was fun.
Now I am working on medical device software development, as the other stuff does not pay the bills.
We currently try to establish something similar on our end using AsciiDoc [1] as fileformat, Antora [2] to build the site and hosting on Azure storage. AsciiDoc has a bit more features compared to Markdown which allows for a richer presentation of the docs.
Biggest difference is that Linode has the docs in a separate repository. Not sure if it is a limitation of their toolchain or a deliberate decision.
Antora allows you to have the project documentation in the actual project repositories. It then pulls the docs from all the different repos together to build the site. This also allows you to have the released product versions go in-synch with the docs versions. Antora builds each version of the product as part of one site. The reader can explore different product versions or navigate between pages across versions.
===
(1) Hue was not a good dimension for encoding magnitude information, i.e. rainbow color maps are bad.
(2) The mechanisms in human vision responsible for high spatial frequency information processing are luminance channels. If the data to be represented have high spatial frequency, use a color map which has a strong luminance variation across the data range.
(3) For interval and ratio data, both luminance- and saturation-varying color maps should produce the effect of having equal steps in data value correspond to equal perceptual steps, but the first will be most effective for high spatial frequency data variations and the second will be most effective for low spatial frequency variations.
===
[1] the original link got removed from IBMs website. Back in the day it was under
https://www.research.ibm.com/people/l/lloydt/color/color.HTM
A pdf copy is here:
https://github.com/frankMilde/interesting-reads/blob/master/...