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SPIN Processed News Frame: The Hype
Feature Interaction Modeling for Physics-Informed Neural Networks and Neural Operators
Researchers introduced feature interaction modules from factorization machines into physics-informed neural networks and neural operators to improve accuracy on parameterized PDEs with strong cross-variable dependencies, especially shock-dominated or discontinuous systems.
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arXiv Machine Learning
Aug 3, 2026
SPIN Processed News Frame: The Fog
Automatic Differentiation from Scratch: How PyTorch Computes Gradients in Physics-Informed Neural Networks
A technical arXiv preprint traces PyTorch’s automatic differentiation mechanics for physics-informed neural networks (PINNs), using explicit numerical walkthroughs and verification against hand derivations to clarify how nested gradients are computed.
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arXiv Machine Learning
Jul 16, 2026