122 citations · 540 across the 65 of their papers we have counts for
4 papers · 1 filter
A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositions
Michael Penwarden, Ameya D. Jagtap, Shandian Zhe +2
Physics-informed neural networks (PINNs) as a means of solving partial differential equations (PDE) have garnered much attention in the Computational Science and Engineering (CS&E)…
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…
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…
Deep Coregionalization for the Emulation of Spatial-Temporal Fields
Wei Xing, Robert M. Kirby, Shandian Zhe
Data-driven surrogate models are widely used for applications such as design optimization and uncertainty quantification, where repeated evaluations of an expensive simulator are r…