Hugging Face Trending Papers
From Numerical Simulators of PDEs to Neural Emulators and Back
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arXiv Machine Learning
Aug 26
From Numerical Simulators of PDEs to Neural Emulators and Back
arXiv:2608.24547v1 Announce Type: new Abstract: Simulation is central to modern engineering and science, but the cost of numerical solvers for partial differential equations (PDEs) remains a bottlene...
arXiv Machine Learning
Oct 8
The Symbol of the Surrogate: Measuring Numerical Provenance in Neural PDE Solvers
arXiv:2610.09255v1 Announce Type: new Abstract: Neural PDE surrogates are trained on numerical solver outputs that contain both physical evolution and solver-specific discretization errors. Because s...
arXiv Machine Learning
Jun 3
Are Deep Learning Based Hybrid PDE Solvers Reliable? Why Training Paradigms and Update Strategies Matter
arXiv:2602. 06842v2 Announce Type: replace-cross Abstract: Deep learning-based hybrid iterative methods (DL-HIMs) integrate classical numerical solvers with neural operators, utilizing their complementary spectral biases to accelerate convergence.
arXiv Machine Learning
Sep 22
Cost-Accuracy Trade-offs: Neural Operator vs Classical Numerical Solver
arXiv:2609.24021v1 Announce Type: cross Abstract: Neural operators are data-driven models that learn mappings from inputs that parameterize partial differential equations, such as spatially varying c...
arXiv AI
Oct 15
Compute-optimal data scaling for neural surrogates via multi-fidelity training
arXiv:2610.17221v1 Announce Type: cross Abstract: Neural surrogates for Partial Differential Equations (PDEs) are usually trained on synthetic data from numerical simulators. As models grow and scali...
Hugging Face Trending Papers
Sep 21
Cost-Accuracy Trade-offs: Neural Operator vs Classical Numerical Solver
Neural operators are data-driven models that learn mappings from inputs that parameterize partial differential equations, such as spatially varying coefficients, initial conditions, forcing terms, bou...