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math.NA2023★ 8 cited
Splitting physics-informed neural networks for inferring the dynamics of integer- and fractional-order neuron models
Simin Shekarpaz, Fanhai Zeng, George Karniadakis
We introduce a new approach for solving forward systems of differential equations using a combination of splitting methods and physics-informed neural networks (PINNs). The propose…
math.NA2022★ 72 cited
Multifidelity Deep Operator Networks For Data-Driven and Physics-Informed Problems
Amanda A. Howard, Mauro Perego, George E. Karniadakis +1
Operator learning for complex nonlinear systems is increasingly common in modeling multi-physics and multi-scale systems. However, training such high-dimensional operators requires…