Sparse RFNNs with sSVD via Lanczos-Golub-Kahan bidiagonalization maintain accuracy while improving efficiency and robustness for 1D steady convection-diffusion equations with strong advection.
Scientific machine learning through physics–informed neural networks: Where we are and what’s next
3 Pith papers cite this work. Polarity classification is still indexing.
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A cross-section-based scaling of the loss function accelerates convergence and improves accuracy for MF-PINNs on neutron diffusion problems across 1D-3D and fixed-source to eigenvalue cases.
φ-DeepONet learns mappings with discontinuities in inputs and outputs by combining multiple branch networks with a nonlinear interface embedding in the trunk, trained via physics- and interface-informed loss, and shows accurate results on 1D/2D benchmarks.
citing papers explorer
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Sparse Random-Feature Neural Networks with Krylov-Based SVD for Singularly Perturbed ODE
Sparse RFNNs with sSVD via Lanczos-Golub-Kahan bidiagonalization maintain accuracy while improving efficiency and robustness for 1D steady convection-diffusion equations with strong advection.
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On Physics-Based Loss Scaling for MF-PINNs applied to the neutron diffusion equation
A cross-section-based scaling of the loss function accelerates convergence and improves accuracy for MF-PINNs on neutron diffusion problems across 1D-3D and fixed-source to eigenvalue cases.
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$\phi-$DeepONet: A Discontinuity Capturing Neural Operator
φ-DeepONet learns mappings with discontinuities in inputs and outputs by combining multiple branch networks with a nonlinear interface embedding in the trunk, trained via physics- and interface-informed loss, and shows accurate results on 1D/2D benchmarks.