A finite-element-inspired neural network with locally focused basis blocks and an adaptive refinement loop solves PDEs with sharp features more accurately than standard PINNs in the reported experiments.
A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems
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Adaptive Basis-inspired Deep Neural Network for Solving Partial Differential Equations with Localized Features
A finite-element-inspired neural network with locally focused basis blocks and an adaptive refinement loop solves PDEs with sharp features more accurately than standard PINNs in the reported experiments.