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[–]Gyoshi 5 points6 points  (2 children)

This is definitely a promising area of active research. I don't know a whole lot about it, but for instance

  1. QuaLiKiz--a quasi-linear solver for estimating transport coefficients for use in integrated modelling--has a neural network surrogate model QLKNN.
  2. Here is one of the latest papers that I happened to have open that goes into DL methods to find reasonable assumptions for their model (aka closure).
  3. This is a relevant line from the above paper: In plasma physics and controlled fusion research, DL techniques have successfully been applied to many problems, such as disruption prediction, the pedestal structure, and equilibrium reconstruction for real-time control.
  4. Here is a youtube video that mentions an attempt to do ML-based non-linear dynamics identification of a spherical tokamak MHD simulation

[–]DarashTheBlackDragon 12 points13 points  (1 child)

Hey everyone! Main author of QLKNN and maintainer of QuaLiKiz here! Working indeed to put the Physics in tokamak machine learning modelling. You can fire me questions if you want! I see you found my gitlab, but I advice to read the (not supergreat) qualikiz website here: qualikiz.com. Please don't hug it to death! :)

[–]ConfusedOrDazed 1 point2 points  (0 children)

qualikiz.com seems to redirect to your gitlab.com wiki?

I don't think we're going to hug gitlab to death, but perhaps it isn't supposed to be redirecting there?

[–][deleted] 3 points4 points  (0 children)

Yes! Been calling it for years! Absolutely some form of AI/machine learning will be used for plasma control!

[–]JacqueBauer 2 points3 points  (0 children)

Big fan of machine learning but always wonder if fusion data sets are too spare for traditional deep learning.

[–]TFenrir[S] 2 points3 points  (0 children)

Looks like this is the paper that shows their work so far

https://www.nature.com/articles/s41586-021-04301-9

Edit: and now a great wired article

https://www.wired.com/story/deepmind-ai-nuclear-fusion/

[–]twohammocks 2 points3 points  (0 children)

Brilliant idea. But i am like you - a layman. I have often thought that if Alphafold is so good at determining exact protein configurations, it should be equally good at determining precise magnetic field configurations as well, right? Esp. if some of those new chirality algorithms - see https://www.scientificamerican.com/article/high-flying-sensor-detects-living-things-from-far-above/ ( could those chirality algorithms be modified to determine the +/- electron spin configuration at each location in the fusion chamber?) And in so doing, allow for more control/containment of fusion reaction? Don't mind me, just thinking aloud.

On a completely different track : AI has really excelled lately at determining noise vs non-noise for acoustical measurements - determining the call of a right whale vs background shipping traffic for example. Perhaps this pattern recognition skill is utilizable in other applications - determining whether quantum inferometry is occuring vs not occuring? I realize i have a rich imaginative life here ;)