Machine-learning custom-made basis functions for partial differential equations

9 November 2021

Papers citing "Machine-learning custom-made basis functions for partial differential equations"

7 / 7 papers shown

Title
Galerkin Neural Networks: A Framework for Approximating Variational Equations with Error Control M. Ainsworth Justin Dong 48 40 0 28 May 2021
Fourier Neural Operator for Parametric Partial Differential Equations Zong-Yi Li Nikola B. Kovachki Kamyar Azizzadenesheli Burigede Liu K. Bhattacharya Andrew M. Stuart Anima Anandkumar AI4CE 509 2,453 0 18 Oct 2020
hp-VPINNs: Variational Physics-Informed Neural Networks With Domain Decomposition E. Kharazmi Zhongqiang Zhang George Karniadakis 176 538 0 11 Mar 2020
Neural Operator: Graph Kernel Network for Partial Differential Equations Zong-Yi Li Nikola B. Kovachki Kamyar Azizzadenesheli Burigede Liu K. Bhattacharya Andrew M. Stuart Anima Anandkumar 208 749 0 07 Mar 2020
VarNet: Variational Neural Networks for the Solution of Partial Differential Equations Reza Khodayi-mehr Michael M. Zavlanos 122 69 0 16 Dec 2019
Variational Physics-Informed Neural Networks For Solving Partial Differential Equations E. Kharazmi Z. Zhang George Karniadakis 87 245 0 27 Nov 2019
DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators Lu Lu Pengzhan Jin George Karniadakis 248 2,158 0 08 Oct 2019