AI pattern recognition was instrumental in the detection of the Higgs boson:
Artificial intelligence called in to tackle LHC data deluge https://www.nature.com/news/artificial-intelligence-called-in-to-tackle-lhc-data-deluge-1.18922
AI pattern recognition deep learning expectation here, however, is an eventual match of LHC->FCC ever-higher energy density pressure hydrodynamic plasma flow peak widths:
Matching the Nonequilibrium Initial Stage of Heavy Ion Collisions to Hydrodynamics with QCD Kinetic Theory Article Matching the Nonequilibrium Initial Stage of Heavy Ion Colli...
with turbulence:
New open release allows theorists to explore LHC data in a new way https://home.cern/news/news/knowledge-sharing/new-open-release-allows-theorists-explore-lhc-data-new-way
as anticipated to some extent by Wolfram:
A New Kind of Science, Notes to p. 1061 https://www.wolframscience.com/nks/notes-9-16--quantum-field-theory/
Indeed, any flow can be modeled in the form of partial differential equation system. There are bundle of studies on applications of AI in chemistry, medical, earth sciences, neurosciences, bio informatics, physics ...are to name a few.
See also some links related to your query : Article Turbulent hydrodynamics experiments in high energy density p...
https://www.springer.com/gp/book/9780792352259
Article Reynolds averaged turbulence modelling using deep neural net...
Yes exactly, thanks for the links. So in the studies of quantum field theory (QFT), the next question is: Will AI Pattern Matching Deep Learning eventually match LHC->FCC ever-higher energy density pressure collision plasma "particle resonance" peak width hydrodynamic flows with Schwinger local field differential turbulence?
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