A measurement is presented of a ratio observable that provides a measure of the azimuthal correlations among jets with large transverse momentum
The modeling of multiscale and multiphysics complex systems typically involves the use of scientific software that can optimally leverage extreme scale computing. Despite major developments in recent years, these simulations continue to be computationally intensive and time consuming. Here we explore the use of AI to accelerate the modeling of complex systems at a fraction of the computational cost of classical methods, and present the first application of physics informed neural operators (NOs) (PINOs) to model 2D incompressible magnetohydrodynamics (MHD) simulations. Our AI models incorporate tensor Fourier NOs as their backbone, which we implemented with the
 Award ID(s):
 1931561
 NSFPAR ID:
 10429345
 Publisher / Repository:
 IOP Publishing
 Date Published:
 Journal Name:
 Machine Learning: Science and Technology
 Volume:
 4
 Issue:
 3
 ISSN:
 26322153
 Page Range / eLocation ID:
 Article No. 035002
 Format(s):
 Medium: X
 Sponsoring Org:
 National Science Foundation
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Abstract The elliptic flow
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