newsNature Machine IntelligenceTrust 88 · LabPublished 22h agoLive · 11h ago
Principled approaches for extending neural architectures to function spaces for operator learning
Nature Machine Intelligence, Published online: 03 July 2026; doi:10.1038/s42256-026-01267-z Berner et al. show how to adapt popular neural networks into discretization-agnostic neural operators that learn from continuous scientific data, enabling scientific simulations that generalize more reliably across resolutions.
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paperSelf-explainable Operator Learning for Discovering Spatial Patterns in Functional DatapaperAn Optimisation Framework for the Well-Conditioned Training of Physics-Informed Neural NetworksrepoSciML/NeuralPDE.jlpaperGAIA: Geometry-Adaptive Operator Learning for Forward and Inverse ProblemspaperError-Conditioned Neural Solvers
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paperError-Conditioned Neural SolverspaperGAIA: Geometry-Adaptive Operator Learning for Forward and Inverse ProblemsrepoSciML/NeuralPDE.jlpaperSelf-explainable Operator Learning for Discovering Spatial Patterns in Functional DatapaperAn Optimisation Framework for the Well-Conditioned Training of Physics-Informed Neural Networks