102 citations · 787 across the 61 of their papers we have counts for
Showing physics.comp-phShow all
2 papers · 1 filter
physics.comp-ph2021
A Metalearning Approach for Physics-Informed Neural Networks (PINNs): Application to Parameterized PDEs
Michael Penwarden, Shandian Zhe, Akil Narayan +1
Physics-informed neural networks (PINNs) as a means of discretizing partial differential equations (PDEs) are garnering much attention in the Computational Science and Engineering…
physics.comp-ph2021★ 73 cited
Multifidelity Modeling for Physics-Informed Neural Networks (PINNs)
Michael Penwarden, Shandian Zhe, Akil Narayan +1
Multifidelity simulation methodologies are often used in an attempt to judiciously combine low-fidelity and high-fidelity simulation results in an accuracy-increasing, cost-saving…