199 citations · 302 across the 61 of their papers we have counts for
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math.NA2023★ 31 cited
Enhancing training of physics-informed neural networks using domain-decomposition based preconditioning strategies
Alena Kopaničáková, Hardik Kothari, George Em Karniadakis +1
We propose to enhance the training of physics-informed neural networks (PINNs). To this aim, we introduce nonlinear additive and multiplicative preconditioning strategies for the w…
cs.LG2023★ 39 cited
Physics-informed neural networks for predicting gas flow dynamics and unknown parameters in diesel engines
Kamaljyoti Nath, Xuhui Meng, Daniel J Smith +1
This paper presents a physics-informed neural network (PINN) approach for monitoring the health of diesel engines. The aim is to evaluate the engine dynamics, identify unknown para…