nelson_software · @nelson_software
3 followers · 32 posts · Server techhub.social
New Submissions to TMLR · @tmlrsub
197 followers · 678 posts · Server sigmoid.social

Physics informed neural networks for elliptic equations with oscillatory differential operators

openreview.net/forum?id=QfyVqv

#elliptic #numerical #gradient

Last updated 1 year ago

The extraction seems to be working really well, but can't tell how well yet

Has two repeating sections. One is very clearly good data that looks as expected

The other looks like noise, but is very repeatable and (partially) points in a way that true noise wouldn't point. Also partially larger than the real effect could be

What I really need is more example data. Fortunately bosses/partners are listening enough to make that happen (maybe)

#numerical #math #space #python #numpy #matplotlib

Last updated 1 year ago

Actually...those effects must not be an issue or I'd already be seeing them at apogee in a high-e .

My interpolation is only non-flat at perigee, which is exactly where pos, vel and acc are changing fastest and methods are going to be noisiest.

So I think I just need to reduce noise well below .1mm/s^2 and I should be good...?

#orbit #numerical

Last updated 1 year ago

The smooth elset is for a high-e orbit For Reasons which also means the hard part is perigee.

My first naive method was to subtract adjacent velocities. That's awful.

Learning some methods, I then tried (v(t+1) - v(t-1))/2t. That gives me a 25 mm/s^2 residual acc. Meh.

Lagrange gives me 5 mm/s^2. Getting somewhere.

Going back to central diffs but using t-2, t-1, t+1, t+2 I do even better at 1.5 mm/s^2 residual.

#numerical #interpolation

Last updated 1 year ago

I am still in this rabbit hole, learning a bunch of new methods.

What I want at the end is *thrust* acceleration values recovered from satellite position and velocity data given at 1m intervals.

Any method will obviously also have the acc due to gravity and other effects. Methods will differ in how much numerical "noise" they add to the process.

Subtracting the Earth is easy with F=ma=GMm/r^2.

To compare the noise, I'm using a smooth element set that has no thrusts.

#numerical #keplerian

Last updated 1 year ago

So.... . They are pretty dang clever and pretty dang amazing. You can get an analytic (if that's the word I want) polynomial fit! That you can take a continuous derivative!

For arbitrary data, I can see why it might not work.

But for a physical object that actually is moving according to a 2nd-order kinematics (piecewise--really 3rd-order overall) but all you have data for the the first two orders, it might be a good way to recover the higher order(s).

#Lagrange #polynomials #math #numerical

Last updated 1 year ago

Stewart Russell · @scruss
232 followers · 1502 posts · Server xoxo.zone

Fun tiny challenge: in a language that supports floating point but doesn't have a defined constant for pi, devise an efficient way of approximating pi without resorting to 4*atan(1)

('s maths library only works in degrees, so

4 1 dup atan mul

returns 180.0)

#numerical #programming #postscript

Last updated 1 year ago

WordofTheHour · @wordofthehour
2265 followers · 35503 posts · Server botsin.space

: belonging to number

- French: numérique

- German: zahlenmäßig

- Italian: numerico

- Portuguese: numérico

- Spanish: numérico

------------

See previous words @ wordofthehour.org/r/past

#numerical

Last updated 1 year ago

nelson_software · @nelson_software
2 followers · 31 posts · Server techhub.social
nelson_software · @nelson_software
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nelson_software · @nelson_software
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nelson_software · @nelson_software
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nelson_software · @nelson_software
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nelson_software · @nelson_software
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nelson_software · @nelson_software
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nelson_software · @nelson_software
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nelson_software · @nelson_software
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@peterrenshaw 🍂 · @peterrenshaw
306 followers · 908 posts · Server ioc.exchange

ChatGPT Gets Its “Wolfram Superpowers”!: “Back in January, I made the point that as an LLM neural net, ChatGPT—for all its remarkable prowess in textually generating material “like” what it’s read from the web, etc can’t itself be expected to do actual nontrivial computations or to systematically produce correct (rather than just “looks roughly right”) , etc.”

/ / / / <writings.stephenwolfram.com/20>

#data #ai #chatgpt #Wolfram #alpha #numerical

Last updated 2 years ago

@peterrenshaw 🍂 · @peterrenshaw
305 followers · 908 posts · Server ioc.exchange

ChatGPT Gets Its “Wolfram Superpowers”!: Back in January, I made the point that, as an LLM neural net, ChatGPT—for all its remarkable prowess in textually generating material “like” what it’s read from the web, etc.—can’t itself be expected to do actual nontrivial computations, or to systematically produce correct (rather than just “looks roughly right”) , etc.

/ / / / <writings.stephenwolfram.com/20>

#data #ai #chatgpt #Wolfram #alpha #numerical

Last updated 2 years ago