DSGNAR optimization framework for PINNs reaches relative L2 errors of 3e-16 in double precision and improves prior results by 5-8 orders of magnitude on Burgers' and high-dimensional Poisson problems while remaining faster.
Finite basis physics-informed neural networks (FBPINNs): a scalable domain decomposition approach for solving differential equa- tions
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An Optimisation Framework for the Well-Conditioned Training of Physics-Informed Neural Networks
DSGNAR optimization framework for PINNs reaches relative L2 errors of 3e-16 in double precision and improves prior results by 5-8 orders of magnitude on Burgers' and high-dimensional Poisson problems while remaining faster.