{"total":27,"rows":[{"id":"2406.02917","title":"A comprehensive and FAIR comparison between MLP and KAN representations for differential equations and operator networks","created":"2024-06-05","cited_by_count":104,"area":null},{"id":"2410.13228","title":"From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learning","created":"2024-10-17","cited_by_count":73,"area":null},{"id":"2402.01960","title":"Calibrated Uncertainty Quantification for Operator Learning via Conformal Prediction","created":"2024-02-02","cited_by_count":26,"area":null},{"id":"2504.03503","title":"Operator Learning: A Statistical Perspective","created":"2025-04-04","cited_by_count":17,"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":"2404.08809","title":"Leveraging viscous Hamilton-Jacobi PDEs for uncertainty quantification in scientific machine learning","created":"2024-04-12","cited_by_count":9,"area":null},{"id":"2405.12380","title":"Fast meta-solvers for 3D complex-shape scatterers using neural operators trained on a non-scattering problem","created":"2024-05-20","cited_by_count":8,"area":null},{"id":"2505.04950","title":"Epistemic Artificial Intelligence is Essential for Machine Learning Models to Truly 'Know When They Do Not Know'","created":"2025-05-08","cited_by_count":8,"area":null},{"id":"2407.21217","title":"NeuroSEM: A hybrid framework for simulating multiphysics problems by coupling PINNs and spectral elements","created":"2024-07-30","cited_by_count":6,"area":null},{"id":"2411.19125","title":"Advancing Generalization in PINNs through Latent-Space Representations","created":"2024-11-28","cited_by_count":5,"area":null},{"id":"2403.03444","title":"Uncertainty quantification for deeponets with ensemble kalman inversion","created":"2024-03-06","cited_by_count":4,"area":null},{"id":"2509.04623","title":"Split Conformal Prediction in the Function Space with Neural Operators","created":"2025-09-04","cited_by_count":4,"area":null},{"id":"2604.17156","title":"Uncertainty Quantification in PINNs for Turbulent Flows: Bayesian Inference and Repulsive Ensembles","created":"2026-04-18","cited_by_count":4,"area":null},{"id":"2408.07201","title":"Quantification of total uncertainty in the physics-informed reconstruction of CVSim-6 physiology","created":"2024-08-13","cited_by_count":3,"area":null},{"id":"2505.06459","title":"Improved Uncertainty Quantification in Physics-Informed Neural Networks Using Error Bounds and Solution Bundles","created":"2025-05-09","cited_by_count":3,"area":null},{"id":"2510.05568","title":"Bilevel optimization for learning hyperparameters: Application to solving PDEs and inverse problems with Gaussian processes","created":"2025-10-07","cited_by_count":3,"area":null},{"id":"2407.02770","title":"Large language models, physics-based modeling, experimental measurements: the trinity of data-scarce learning of polymer properties","created":"2024-07-03","cited_by_count":2,"area":null},{"id":"2501.09298","title":"Physics-informed deep learning for infectious disease forecasting","created":"2025-01-16","cited_by_count":2,"area":null},{"id":"2505.19136","title":"Uncertainty Quantification for Physics-Informed Neural Networks with Extended Fiducial Inference","created":"2025-05-25","cited_by_count":2,"area":null},{"id":"2507.06967","title":"Noisy PDE Training Requires Bigger PINNs","created":"2025-07-09","cited_by_count":2,"area":null},{"id":"2509.14219","title":"Data Denoising and Derivative Estimation for Data-Driven Modeling of Nonlinear Dynamical Systems","created":"2025-09-17","cited_by_count":1,"area":null},{"id":"2605.16966","title":"Harnessing AI for Inverse Partial Differential Equation Problems: Past, Present, and Prospects","created":"2026-05-16","cited_by_count":1,"area":null},{"id":"2602.01176","title":"Multi-Fidelity Physics-Informed Neural Networks with Bayesian Uncertainty Quantification and Adaptive Residual Learning for Efficient Solution of Parametric Partial Differential Equations","created":"2026-02-01","cited_by_count":0,"area":null},{"id":"2607.14193","title":"Operator-Informed Gaussian Processes for Complex Helmholtz Wavefields: From Synthetic Benchmarks to In Vivo Brain Elastography","created":"2026-07-15","cited_by_count":0,"area":null},{"id":"2608.12624","title":"Structure-preserving uncertainty quantification for GENERIC dynamics","created":"2026-08-12","cited_by_count":0,"area":null},{"id":"2608.20483","title":"Uncertainty propagation in auto-regressive random neural network models","created":"2026-08-20","cited_by_count":0,"area":null},{"id":"2609.07752","title":"Local gradient neural operator","created":"2026-09-07","cited_by_count":0,"area":null}],"next_cursor":null}