Physics-constrained machine learning for electrodynamics without gauge ambiguity based on Fourier transformed Maxwell's equations
C Leon, A Scheinker - Scientific Reports, 2024 - nature.com
We utilize a Fourier transformation-based representation of Maxwell's equations to develop
physics-constrained neural networks for electrodynamics without gauge ambiguity, which …
physics-constrained neural networks for electrodynamics without gauge ambiguity, which …
The coupled physical-informed neural networks for the two phase magnetohydrodynamic flows
K Peng, J Li - Computers & Mathematics with Applications, 2024 - Elsevier
In this paper, we present the coupled physics-informed neural networks (CPINNs) for solving
the two phase magnetohydrodynamic flows which is governed by Cahn–Hilliard equation …
the two phase magnetohydrodynamic flows which is governed by Cahn–Hilliard equation …
[PDF][PDF] GENERALIZATION ABILITY OF CONVOLUTIONAL NEURAL NETWORKS TRAINED FOR COHERENT SYNCHROTRON RADIATION COMPUTATIONS
C Leon, PM Anisimov, N Yampolsky… - 32nd Linear Accelerator …, 2024 - inspirehep.net
Coherent synchrotron radiation (CSR) has a significant impact on electron storage rings and
bunch compressors, inducing energy spread and emittance growth in a bunch. Calculating …
bunch compressors, inducing energy spread and emittance growth in a bunch. Calculating …
[PDF][PDF] UTILIZING NEURAL NETWORKS TO SPEED UP COHERENT SYNCHROTRON RADIATION COMPUTATIONS
C Leon, PM Anisimov, N Yampolsky, A Scheinker - Training - jacow.org
Coherent synchrotron radiation has a significant impact on electron storage rings and bunch
compressors, inducing energy spread and emittance growth in a bunch. While the physics of …
compressors, inducing energy spread and emittance growth in a bunch. While the physics of …
[PDF][PDF] SOLVING THE ORSZAG-TANG VORTEX MAGNETOHYDRODYNAMICS PROBLEM WITH PHYSICS-CONSTRAINED CONVOLUTIONAL NEURAL …
C Leon, A Scheinker, A Bormanis - jacow.org
Abstract The 2D Orszag-Tang vortex magnetohydrodynamics (MHD) problem is studied
through the use of physicsconstrained convolutional neural networks (PCNNs). The density …
through the use of physicsconstrained convolutional neural networks (PCNNs). The density …