MS-SFNN builds PDE solutions from element-wise products of outputs from d independent fixed-random-weight subnetworks with tunable scaling and cosine activations, then solves coefficients by least squares, claiming superior accuracy on high-frequency problems.
Randomized neural networks with petrov- galerkin methods for solving linear elasticity problems.arXiv preprint arXiv:2308.03088, 2023
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TPNet constructs multi-dimensional basis functions via tensor products of subnetwork outputs and solves for coefficients with least-squares to solve PDEs more efficiently than PINNs.
citing papers explorer
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Multi-Scale Separable Fourier Neural Networks for Solving High-Frequency PDEs
MS-SFNN builds PDE solutions from element-wise products of outputs from d independent fixed-random-weight subnetworks with tunable scaling and cosine activations, then solves coefficients by least squares, claiming superior accuracy on high-frequency problems.
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A Novel Tensor Product-Based Neural Network for Solving Partial Differential Equations
TPNet constructs multi-dimensional basis functions via tensor products of subnetwork outputs and solves for coefficients with least-squares to solve PDEs more efficiently than PINNs.