{"total":23,"rows":[{"id":"2402.01868","title":"Challenges in Training PINNs: A Loss Landscape Perspective","created":"2024-02-02","cited_by_count":113,"area":null},{"id":"2207.02338","title":"Mitigating Propagation Failures in Physics-informed Neural Networks using Retain-Resample-Release (R3) Sampling","created":"2022-07-05","cited_by_count":73,"area":null},{"id":"2406.19662","title":"Finite basis Kolmogorov-Arnold networks: domain decomposition for data-driven and physics-informed problems","created":"2024-06-28","cited_by_count":35,"area":null},{"id":"2404.07662","title":"PINNACLE: PINN Adaptive ColLocation and Experimental points selection","created":"2024-04-11","cited_by_count":32,"area":null},{"id":"2311.06483","title":"Stacked networks improve physics-informed training: applications to neural networks and deep operator networks","created":"2023-11-11","cited_by_count":21,"area":null},{"id":"2304.03894","title":"A multifidelity approach to continual learning for physical systems","created":"2023-04-08","cited_by_count":13,"area":null},{"id":"2306.00230","title":"Predictive Limitations of Physics-Informed Neural Networks in Vortex Shedding","created":"2023-05-31","cited_by_count":12,"area":null},{"id":"2401.08667","title":"Data-Driven Physics-Informed Neural Networks: A Digital Twin Perspective","created":"2024-01-05","cited_by_count":12,"area":null},{"id":"2411.06286","title":"SPIKANs: Separable Physics-Informed Kolmogorov-Arnold Networks","created":"2024-11-09","cited_by_count":12,"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":"2302.07557","title":"On the Generalization of PINNs outside the training domain and the Hyperparameters influencing it","created":"2023-02-15","cited_by_count":10,"area":null},{"id":"2410.23667","title":"Projected Neural Differential Equations for Learning Constrained Dynamics","created":"2024-10-31","cited_by_count":5,"area":null},{"id":"2401.07888","title":"Multifidelity domain decomposition-based physics-informed neural networks and operators for time-dependent problems","created":"2024-01-15","cited_by_count":4,"area":null},{"id":"2405.08558","title":"PTPI-DL-ROMs: pre-trained physics-informed deep learning-based reduced order models for nonlinear parametrized PDEs","created":"2024-05-14","cited_by_count":4,"area":null},{"id":"2310.05169","title":"Investigating the Ability of PINNs To Solve Burgers' PDE Near Finite-Time BlowUp","created":"2023-10-08","cited_by_count":2,"area":null},{"id":"2506.16443","title":"Leveraging Influence Functions for Resampling Data in Physics-Informed Neural Networks","created":"2025-06-19","cited_by_count":2,"area":null},{"id":"2409.08958","title":"PINNfluence: Interpreting PINNs through Influence Functions","created":"2024-09-13","cited_by_count":1,"area":null},{"id":"2512.04310","title":"RNNs perform task computations by dynamically warping neural representations","created":"2025-12-03","cited_by_count":1,"area":null},{"id":"2605.11316","title":"Error whitening: Why Gauss-Newton outperforms Newton","created":"2026-05-11","cited_by_count":1,"area":null},{"id":"2507.01714","title":"B-PL-PINN: Stabilizing PINN Training with Bayesian Pseudo Labeling","created":"2025-07-02","cited_by_count":0,"area":null},{"id":"2509.07245","title":"IP-Basis PINNs: Efficient Multi-Query Inverse Parameter Estimation","created":"2025-09-08","cited_by_count":0,"area":null},{"id":"2509.11768","title":"Stabilizing PINNs: A regularization scheme for PINN training to avoid unstable fixed points of dynamical systems","created":"2025-09-15","cited_by_count":0,"area":null},{"id":"2511.00792","title":"Fast PINN Eigensolvers via Biconvex Reformulation","created":"2025-11-02","cited_by_count":0,"area":null}],"next_cursor":null}