MS-SFNN encodes multi-scale Fourier features in a separable product of fixed-weight cosine subnetworks and solves for linear coefficients by least squares, claiming better accuracy than PINN and SV-SNN on high-frequency PDEs.
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 encodes multi-scale Fourier features in a separable product of fixed-weight cosine subnetworks and solves for linear coefficients by least squares, claiming better accuracy than PINN and SV-SNN on high-frequency PDEs.
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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.