arXiv Machine Learning
From Numerical Simulators of PDEs to Neural Emulators and Back
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From Numerical Simulators of PDEs to Neural Emulators and Back
Simulation is central to modern engineering and science, but the cost of numerical solvers for partial differential equations (PDEs) remains a bottleneck whenever fast or many-query evaluations are re...
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The Symbol of the Surrogate: Measuring Numerical Provenance in Neural PDE Solvers
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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...
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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.
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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...
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Operator Boosting Produces Pareto-Efficient PDE Surrogates
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