A separable Fourier-feature neural network with learnable frequencies and a three-level frequency sampler is reported to solve high-frequency PDEs with far fewer parameters than vanilla PINNs.
Multi-level physics informed deep learning for solving partial differential equations in computational structural mechanics
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Separated-Variable Spectral Neural Networks: A Physics-Informed Learning Approach for High-Frequency PDEs
A separable Fourier-feature neural network with learnable frequencies and a three-level frequency sampler is reported to solve high-frequency PDEs with far fewer parameters than vanilla PINNs.