{"total":19,"rows":[{"id":"2308.05882","title":"GPLaSDI: Gaussian Process-based Interpretable Latent Space Dynamics Identification through Deep Autoencoder","created":"2023-08-10","cited_by_count":30,"area":null},{"id":"2309.04699","title":"Weak-PDE-LEARN: A Weak Form Based Approach to Discovering PDEs From Noisy, Limited Data","created":"2023-09-09","cited_by_count":16,"area":null},{"id":"2409.00604","title":"Spatio-spectral graph neural operator for solving computational mechanics problems on irregular domain and unstructured grid","created":"2024-09-01","cited_by_count":6,"area":null},{"id":"2511.08860","title":"When is a System Discoverable from Data? Discovery Requires Chaos","created":"2025-11-12","cited_by_count":6,"area":null},{"id":"2502.18377","title":"Mechanistic PDE Networks for Discovery of Governing Equations","created":"2025-02-25","cited_by_count":4,"area":null},{"id":"2606.09638","title":"Data-driven discovery of governing differential equations across physical systems","created":"2026-06-08","cited_by_count":3,"area":null},{"id":"2401.17177","title":"Data-Driven Discovery of PDEs via the Adjoint Method","created":"2024-01-30","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":"2512.15997","title":"Higher-Order LaSDI: Reduced Order Modeling with Multiple Time Derivatives","created":"2025-12-17","cited_by_count":2,"area":null},{"id":"2512.23761","title":"Learning Coupled System Dynamics under Incomplete Physical Constraints and Missing Data","created":"2025-12-28","cited_by_count":2,"area":null},{"id":"2604.18548","title":"Physics-Informed Neural Networks for Biological $2\\mathrm{D}{+}t$ Reaction-Diffusion Systems","created":"2026-04-20","cited_by_count":2,"area":null},{"id":"2501.18715","title":"chebgreen: Learning and Interpolating Continuous Empirical Green's Functions from Data","created":"2025-01-30","cited_by_count":1,"area":null},{"id":"2504.07618","title":"CTSR: Cartesian tensor-based sparse regression for data-driven discovery of high-dimensional invariant governing equations","created":"2025-04-10","cited_by_count":1,"area":null},{"id":"2603.22951","title":"Weak-PDE-Net: Discovering Open-Form PDEs via Differentiable Symbolic Networks and Weak Formulation","created":"2026-03-24","cited_by_count":1,"area":null},{"id":"2309.16131","title":"A Spectral Approach for Learning Spatiotemporal Neural Differential Equations","created":"2023-09-28","cited_by_count":0,"area":null},{"id":"2409.07942","title":"Taylor-Sensus Network: Embracing Noise to Enlighten Uncertainty for Scientific Data","created":"2024-09-12","cited_by_count":0,"area":null},{"id":"2607.23753","title":"On the post-hoc Evaluation of PDE Discovery: A Multifaceted Challenge of Scientific Advancement","created":"2026-07-26","cited_by_count":0,"area":null},{"id":"2607.29665","title":"Freeze, Then Select: Structured Field Adapters and Stability-Validated Weak Selection for PDE Discovery from Sparse Observations","created":"2026-07-31","cited_by_count":0,"area":null},{"id":"2608.27475","title":"Hypothesize, Evaluate, Refine: A Scientific Agent for PDE Discovery with Unknown Spatial Coefficient Fields","created":"2026-08-19","cited_by_count":0,"area":null}],"next_cursor":null}