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.
A modern take on the bias-variance tradeoff in neural networks
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
A data-driven framework using normalizing flows predicts the rate and kinematic distributions of dark photon and millicharged particle production directly from measured dilepton events.
Simulations show that least-squares interpolation on contaminated data exhibits double descent with superior generalization over robust alternatives at high overparameterization.
citing papers explorer
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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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Data-Driven Predictions for Dark Photon and Millicharged Particle Production
A data-driven framework using normalizing flows predicts the rate and kinematic distributions of dark photon and millicharged particle production directly from measured dilepton events.
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Double descent for least-squares interpolation on contaminated data: A simulation study
Simulations show that least-squares interpolation on contaminated data exhibits double descent with superior generalization over robust alternatives at high overparameterization.