2 citations · 3 across the 3 of their papers we have counts for
4 papers
Scalable algorithms for physics-informed neural and graph networks
Khemraj Shukla, Mengjia Xu, Nathaniel Trask +1
Physics-informed machine learning (PIML) has emerged as a promising new approach for simulating complex physical and biological systems that are governed by complex multiscale proc…
Parallel Physics-Informed Neural Networks via Domain Decomposition
Khemraj Shukla, Ameya D. Jagtap, George Em Karniadakis
We develop a distributed framework for the physics-informed neural networks (PINNs) based on two recent extensions, namely conservative PINNs (cPINNs) and extended PINNs (XPINNs),…
A high order discontinuous Galerkin method for the symmetric form of the anisotropic viscoelastic wave equation
Khemraj Shukla, Jesse Chan, Maarten V. de Hoop
Wave propagation in real media is affected by various non-trivial physical phenomena, e.g., anisotropy, an-elasticity and dissipation. Assumptions on the stress-strain relationship…
Physics-informed neural network for ultrasound nondestructive quantification of surface breaking cracks
Khemraj Shukla, Patricio Clark Di Leoni, James Blackshire +2
We introduce an optimized physics-informed neural network (PINN) trained to solve the problem of identifying and characterizing a surface breaking crack in a metal plate. PINNs are…