Search Results for author: Tarje Nissen-Meyer

Found 2 papers, 1 papers with code

Scaling physics-informed neural networks to large domains by using domain decomposition

no code implementations NeurIPS Workshop DLDE 2021 Ben Moseley, Andrew Markham, Tarje Nissen-Meyer

Recently, physics-informed neural networks (PINNs) have offered a powerful new paradigm for solving forward and inverse problems relating to differential equations.

Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations

1 code implementation16 Jul 2021 Ben Moseley, Andrew Markham, Tarje Nissen-Meyer

FBINNs are designed to address the spectral bias of neural networks by using separate input normalisation over each subdomain, and reduce the complexity of the underlying optimisation problem by using many smaller neural networks in a parallel divide-and-conquer approach.

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