{"total":34,"rows":[{"id":"2201.05624","title":"Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next","created":"2022-01-14","cited_by_count":608,"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":"2302.04107","title":"Can Physics-Informed Neural Networks beat the Finite Element Method?","created":"2023-02-08","cited_by_count":68,"area":null},{"id":"2109.09338","title":"Learning in Sinusoidal Spaces with Physics-Informed Neural Networks","created":"2021-09-20","cited_by_count":62,"area":null},{"id":"2211.05567","title":"Partial Differential Equations Meet Deep Neural Networks: A Survey","created":"2022-10-27","cited_by_count":25,"area":null},{"id":"2211.07377","title":"Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks in Scientific Computing","created":"2022-11-14","cited_by_count":24,"area":null},{"id":"2108.13137","title":"Multiscale modeling of inelastic materials with Thermodynamics-based Artificial Neural Networks (TANN)","created":"2021-08-30","cited_by_count":18,"area":null},{"id":"2207.14291","title":"Physics-Informed Neural Networks for Shell Structures","created":"2022-07-26","cited_by_count":13,"area":null},{"id":"2503.06320","title":"Learning and discovering multiple solutions using physics-informed neural networks with random initialization and deep ensemble","created":"2025-03-08","cited_by_count":13,"area":null},{"id":"2301.04480","title":"BINN: A deep learning approach for computational mechanics problems based on boundary integral equations","created":"2023-01-11","cited_by_count":11,"area":null},{"id":"2110.01359","title":"CENN: Conservative energy method based on neural networks with subdomains for solving variational problems involving heterogeneous and complex geometries","created":"2021-09-25","cited_by_count":10,"area":null},{"id":"2310.20425","title":"Discussing the Spectrum of Physics-Enhanced Machine Learning; a Survey on Structural Mechanics Applications","created":"2023-10-31","cited_by_count":7,"area":null},{"id":"2404.13646","title":"Physics-informed Discretization-independent Deep Compositional Operator Network","created":"2024-04-21","cited_by_count":6,"area":null},{"id":"2302.01538","title":"DCEM: A deep complementary energy method for solid mechanics","created":"2023-02-03","cited_by_count":5,"area":null},{"id":"2411.03671","title":"Energy-based physics-informed neural network for frictionless contact problems under large deformation","created":"2024-11-06","cited_by_count":4,"area":null},{"id":"2308.15640","title":"Identifying Constitutive Parameters for Complex Hyperelastic Materials using Physics-Informed Neural Networks","created":"2023-08-29","cited_by_count":3,"area":null},{"id":"2312.06993","title":"Dynamically configured physics-informed neural network in topology optimization applications","created":"2023-12-12","cited_by_count":3,"area":null},{"id":"2405.00055","title":"A Hybrid Probabilistic Battery Health Management Approach for Robust Inspection Drone Operations","created":"2024-04-24","cited_by_count":2,"area":null},{"id":"2405.18311","title":"Deterministic and statistical calibration of constitutive models from full-field data with parametric physics-informed neural networks","created":"2024-05-28","cited_by_count":2,"area":null},{"id":"2211.15664","title":"Utilising physics-guided deep learning to overcome data scarcity","created":"2022-11-24","cited_by_count":1,"area":null},{"id":"2412.04596","title":"Learning Nonlinear Finite Element Solution Operators using Multilayer Perceptrons and Energy Minimization","created":"2024-12-05","cited_by_count":1,"area":null},{"id":"2607.20378","title":"PG-KINN: A Physics-Informed Petrov-Galerkin Kolmogorov-Arnold Network for Solving Forward and Inverse PDEs","created":"2026-07-22","cited_by_count":1,"area":null},{"id":"2110.10724","title":"Semi-supervised physics guided deep learning framework for predicting the I-V characteristics of GAN HEMT","created":"2021-10-20","cited_by_count":0,"area":null},{"id":"2211.16699","title":"Interpretability and accessibility of machine learning in selected food processing, agriculture and health applications","created":"2022-11-30","cited_by_count":0,"area":null},{"id":"2304.06234","title":"Physics-informed radial basis network (PIRBN): A local approximating neural network for solving nonlinear PDEs","created":"2023-04-13","cited_by_count":0,"area":null},{"id":"2308.00927","title":"Physics-informed neural networks for blood flow inverse problems","created":"2023-08-02","cited_by_count":0,"area":null},{"id":"2405.03427","title":"Geometry-aware framework for deep energy method: an application to structural mechanics with hyperelastic materials","created":"2024-05-06","cited_by_count":0,"area":null},{"id":"2410.07605","title":"A Variational Bayesian Inference Theory of Elasticity and Its Mixed Probabilistic Finite Element Method for Inverse Deformation Solutions in Any Dimension","created":"2024-10-10","cited_by_count":0,"area":null},{"id":"2504.18854","title":"Predicting Stress in Two-phase Random Materials and Super-Resolution Method for Stress Images by Embedding Physical Information","created":"2025-04-26","cited_by_count":0,"area":null},{"id":"2506.12922","title":"PINNs Algorithmic Framework for Simulation of Nonlinear Burgers' Type Models","created":"2025-06-15","cited_by_count":0,"area":null},{"id":"2603.03832","title":"Non-Invasive Reconstruction of Cardiac Activation Dynamics Using Physics-Informed Neural Networks","created":"2026-03-04","cited_by_count":0,"area":null},{"id":"2603.26030","title":"Constitutive parameterized deep energy method for solid mechanics problems with random material parameters","created":"2026-03-27","cited_by_count":0,"area":null},{"id":"2606.16624","title":"GA-VINO: A Geometry-Aware Variational Physics-informed Neural Operator for Mindlin-Reissner Plates","created":"2026-06-15","cited_by_count":0,"area":null},{"id":"2609.10983","title":"A variational physics-informed graph neural network for heterogeneous solid mechanics","created":"2026-09-10","cited_by_count":0,"area":null}],"next_cursor":null}