{"total":31,"rows":[{"id":"2401.02398","title":"Generating synthetic data for neural operators","created":"2024-01-04","cited_by_count":11,"area":null},{"id":"2501.06572","title":"Evolutionary Optimization of Physics-Informed Neural Networks: Evo-PINN Frontiers and Opportunities","created":"2025-01-11","cited_by_count":11,"area":null},{"id":"2202.02710","title":"Spectrally Adapted Physics-Informed Neural Networks for Solving Unbounded Domain Problems","created":"2022-02-06","cited_by_count":9,"area":null},{"id":"2509.13805","title":"Towards a Physics Foundation Model","created":"2025-09-17","cited_by_count":8,"area":null},{"id":"2210.12669","title":"Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks","created":"2022-10-23","cited_by_count":6,"area":null},{"id":"2601.14517","title":"Learning PDE Solvers with Physics and Data: A Unifying View of Physics-Informed Neural Networks and Neural Operators","created":"2026-01-20","cited_by_count":6,"area":null},{"id":"2312.03243","title":"Evolutionary Optimization of Physics-Informed Neural Networks: Advancing Generalizability by the Baldwin Effect","created":"2023-12-06","cited_by_count":5,"area":null},{"id":"2402.08313","title":"Approximating Families of Sharp Solutions to Fisher's Equation with Physics-Informed Neural Networks","created":"2024-02-13","cited_by_count":5,"area":null},{"id":"2502.16667","title":"MetaSym: A Symplectic Meta-learning Framework for Physical Intelligence","created":"2025-02-23","cited_by_count":5,"area":null},{"id":"2503.18181","title":"Adaptive Physics-informed Neural Networks: A Survey","created":"2025-03-23","cited_by_count":5,"area":null},{"id":"2601.19091","title":"Out-of-Distribution Generalization for Neural Physics Solvers","created":"2026-01-27","cited_by_count":5,"area":null},{"id":"2304.11702","title":"Controlled physics-informed data generation for deep learning-based remaining useful life prediction under unseen operation conditions","created":"2023-04-23","cited_by_count":4,"area":null},{"id":"2402.00825","title":"Resolution invariant deep operator network for PDEs with complex geometries","created":"2024-02-01","cited_by_count":4,"area":null},{"id":"2402.03153","title":"Learning solutions of parametric Navier-Stokes with physics-informed neural networks","created":"2024-02-05","cited_by_count":4,"area":null},{"id":"2304.04854","title":"iPINNs: Incremental learning for Physics-informed neural networks","created":"2023-04-10","cited_by_count":3,"area":null},{"id":"2308.02599","title":"Branched Latent Neural Maps","created":"2023-08-04","cited_by_count":3,"area":null},{"id":"2405.01480","title":"Common pitfalls to avoid while using multiobjective optimization in machine learning","created":"2024-05-02","cited_by_count":2,"area":null},{"id":"2412.00225","title":"Meta-learning Loss Functions of Parametric Partial Differential Equations Using Physics-Informed Neural Networks","created":"2024-11-29","cited_by_count":2,"area":null},{"id":"2402.11126","title":"Kolmogorov n-Widths for Multitask Physics-Informed Machine Learning (PIML) Methods: Towards Robust Metrics","created":"2024-02-16","cited_by_count":1,"area":null},{"id":"2405.11752","title":"Towards Foundation Model for Chemical Reactor Modeling: Meta-Learning with Physics-Informed Adaptation","created":"2024-05-20","cited_by_count":1,"area":null},{"id":"2510.01039","title":"Gated X-TFC: Soft Domain Decomposition for Forward and Inverse Problems in Sharp-Gradient PDEs","created":"2025-10-01","cited_by_count":1,"area":null},{"id":"2602.11630","title":"Neuro-Symbolic Multitasking: A Unified Framework for Discovering Generalizable Solutions to PDE Families","created":"2026-02-12","cited_by_count":1,"area":null},{"id":"2609.02982","title":"Equation Recast for Canonical Operator Learning Across Parametric PDEs","created":"2026-09-02","cited_by_count":1,"area":null},{"id":"2504.21328","title":"Multi-level datasets training method in Physics-Informed Neural Networks","created":"2025-04-30","cited_by_count":0,"area":null},{"id":"2505.01391","title":"Learning and Transferring Physical Models through Derivatives","created":"2025-05-02","cited_by_count":0,"area":null},{"id":"2506.12996","title":"Latent Representation Learning of Multi-scale Thermophysics: Application to Dynamics in Shocked Porous Energetic Material","created":"2025-06-15","cited_by_count":0,"area":null},{"id":"2506.17582","title":"LFR-PINO: A Layered Fourier Reduced Physics-Informed Neural Operator for Parametric PDEs","created":"2025-06-21","cited_by_count":0,"area":null},{"id":"2511.21414","title":"SUPN: Shallow Universal Polynomial Networks for Efficient Function Approximation","created":"2025-11-26","cited_by_count":0,"area":null},{"id":"2604.09289","title":"Meta-Learned Basis Adaptation for Parametric Linear PDEs","created":"2026-04-10","cited_by_count":0,"area":null},{"id":"2605.03511","title":"Meta-Inverse Physics-Informed Neural Networks for High-Dimensional Ordinary Differential Equations","created":"2026-05-05","cited_by_count":0,"area":null},{"id":"2610.19254","title":"Beyond Time Induction: Structured Conditional Neural Solvers for Parametric Stochastic-Volatility Pricing","created":"2026-10-13","cited_by_count":0,"area":null}],"next_cursor":null}