{"total":17,"rows":[{"id":"2412.00568","title":"The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning","created":"2024-11-30","cited_by_count":145,"area":null},{"id":"2302.11002","title":"Learning Physical Models that Can Respect Conservation Laws","created":"2023-02-21","cited_by_count":59,"area":null},{"id":"2505.08783","title":"CodePDE: An Inference Framework for LLM-driven PDE Solver Generation","created":"2025-05-13","cited_by_count":39,"area":null},{"id":"2408.12171","title":"Recent Advances on Machine Learning for Computational Fluid Dynamics: A Survey","created":"2024-08-22","cited_by_count":28,"area":null},{"id":"2404.07200","title":"Toward a Better Understanding of Fourier Neural Operators from a Spectral Perspective","created":"2024-04-10","cited_by_count":25,"area":null},{"id":"2312.11176","title":"Harnessing the Power of Neural Operators with Automatically Encoded Conservation Laws","created":"2023-12-18","cited_by_count":24,"area":null},{"id":"2403.10642","title":"Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs","created":"2024-03-15","cited_by_count":15,"area":null},{"id":"2407.20801","title":"AhmedML: High-Fidelity Computational Fluid Dynamics Dataset for Incompressible, Low-Speed Bluff Body Aerodynamics","created":"2024-07-30","cited_by_count":15,"area":null},{"id":"2310.05227","title":"Physics-aware Machine Learning Revolutionizes Scientific Paradigm for Machine Learning and Process-based Hydrology","created":"2023-10-08","cited_by_count":11,"area":null},{"id":"2506.07003","title":"End-to-End Probabilistic Framework for Learning with Hard Constraints","created":"2025-06-08","cited_by_count":9,"area":null},{"id":"2601.07384","title":"CompNO: A Novel Foundation Model approach for solving Partial Differential Equations","created":"2026-01-12","cited_by_count":5,"area":null},{"id":"2410.01065","title":"Structure-Preserving Operator Learning","created":"2024-10-01","cited_by_count":3,"area":null},{"id":"2511.12890","title":"Method of Manufactured Learning for Solver-free Training of Neural Operators","created":"2025-11-17","cited_by_count":3,"area":null},{"id":"2602.04923","title":"Imposing Boundary Conditions on Neural Operators via Learned Function Extensions","created":"2026-02-04","cited_by_count":3,"area":null},{"id":"2506.19396","title":"Maximal Update Parametrization and Zero-Shot Hyperparameter Transfer for Fourier Neural Operators","created":"2025-06-24","cited_by_count":1,"area":null},{"id":"2607.21932","title":"Generalized Neural Operator for Parametric and Boundary-Value Problems","created":"2026-07-24","cited_by_count":1,"area":null},{"id":"2608.27256","title":"Enforcing Dirichlet Boundary Conditions in Operator Learning","created":"2026-08-27","cited_by_count":1,"area":null}],"next_cursor":null}