Adaptive collocation point selection for PINNs via QR-DEIM residual snapshots reduces relative L2 error on wave, convection, Allen-Cahn, and Burgers' benchmarks.
Physics-informed neu- ral networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,
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Adaptive Collocation Point Strategies For Physics Informed Neural Networks via the QR Discrete Empirical Interpolation Method
Adaptive collocation point selection for PINNs via QR-DEIM residual snapshots reduces relative L2 error on wave, convection, Allen-Cahn, and Burgers' benchmarks.