199 citations · 302 across the 61 of their papers we have counts for
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cs.LG2019★ 6 cited
Learning functionals via LSTM neural networks for predicting vessel dynamics in extreme sea states
José del Águila Ferrandis, Michael Triantafyllou, Chryssostomos Chryssostomidis +1
Predicting motions of vessels in extreme sea states represents one of the most challenging problems in naval hydrodynamics. It involves computing complex nonlinear wave-body intera…
cs.NE2019★ 199 cited
Variational Physics-Informed Neural Networks For Solving Partial Differential Equations
E. Kharazmi, Z. Zhang, G. E. Karniadakis
Physics-informed neural networks (PINNs) [31] use automatic differentiation to solve partial differential equations (PDEs) by penalizing the PDE in the loss function at a random se…