{"total":13,"rows":[{"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":"2211.07871","title":"DINER: Disorder-Invariant Implicit Neural Representation","created":"2022-11-15","cited_by_count":30,"area":null},{"id":"2304.00837","title":"Disorder-invariant Implicit Neural Representation","created":"2023-04-03","cited_by_count":27,"area":null},{"id":"2110.03049","title":"Physics-informed neural network simulation of multiphase poroelasticity using stress-split sequential training","created":"2021-10-06","cited_by_count":26,"area":null},{"id":"2203.09789","title":"Constitutive model characterization and discovery using physics-informed deep learning","created":"2022-03-18","cited_by_count":17,"area":null},{"id":"2405.06443","title":"Residual-based Attention Physics-informed Neural Networks for Spatio-Temporal Ageing Assessment of Transformers Operated in Renewable Power Plants","created":"2024-05-10","cited_by_count":11,"area":null},{"id":"2106.11451","title":"Physics-constrained deep neural network method for estimating parameters in a redox flow battery","created":"2021-06-21","cited_by_count":9,"area":null},{"id":"2203.01514","title":"Physics-informed neural network solution of thermo-hydro-mechanical (THM) processes in porous media","created":"2022-03-03","cited_by_count":8,"area":null},{"id":"2305.03184","title":"A Generative Modeling Framework for Inferring Families of Biomechanical Constitutive Laws in Data-Sparse Regimes","created":"2023-05-04","cited_by_count":8,"area":null},{"id":"2201.13389","title":"Physics-informed neural networks for non-Newtonian fluid thermo-mechanical problems: an application to rubber calendering process","created":"2022-01-31","cited_by_count":6,"area":null},{"id":"2407.20669","title":"A Tutorial on the Use of Physics-Informed Neural Networks to Compute the Spectrum of Quantum Systems","created":"2024-07-30","cited_by_count":2,"area":null},{"id":"2510.23501","title":"Training Deep Physics-Informed Kolmogorov-Arnold Networks","created":"2025-10-27","cited_by_count":2,"area":null},{"id":"2606.10686","title":"An adaptive framework for the axisymmetric pulsar magnetosphere using physics-informed Kolmogorov-Arnold networks","created":"2026-06-09","cited_by_count":0,"area":null}],"next_cursor":null}