A Green-integral neural solver enforces wave physics via nonlocal integral constraints and FFT acceleration to solve the Helmholtz equation more efficiently than standard PINNs on heterogeneous seismic benchmarks.
Mathematical and Scientific Machine Learning , pages=
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A hybrid least squares / gradient descent method accelerates MIONet training by exploiting multilinear structure in last-layer branch parameters via alternating least squares with Kronecker/Khatri-Rao factorization.
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A Green-Integral-Constrained Neural Solver with Stochastic Physics-Informed Regularization
A Green-integral neural solver enforces wave physics via nonlocal integral constraints and FFT acceleration to solve the Helmholtz equation more efficiently than standard PINNs on heterogeneous seismic benchmarks.
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Hybrid Least Squares/Gradient Descent Methods for MIONets
A hybrid least squares / gradient descent method accelerates MIONet training by exploiting multilinear structure in last-layer branch parameters via alternating least squares with Kronecker/Khatri-Rao factorization.