{"total":46,"rows":[{"id":"2206.13103","title":"A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: comparison with finite element method","created":"2022-06-27","cited_by_count":33,"area":null},{"id":"2211.00713","title":"MAgNET: A Graph U-Net Architecture for Mesh-Based Simulations","created":"2022-11-01","cited_by_count":26,"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":"2309.15421","title":"Deep Learning in Deterministic Computational Mechanics","created":"2023-09-27","cited_by_count":21,"area":null},{"id":"2303.07647","title":"Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review","created":"2023-03-14","cited_by_count":15,"area":null},{"id":"2302.04954","title":"Mixed formulation of physics-informed neural networks for thermo-mechanically coupled systems and heterogeneous domains","created":"2023-02-09","cited_by_count":14,"area":null},{"id":"2311.06380","title":"Theory and implementation of inelastic Constitutive Artificial Neural Networks","created":"2023-11-10","cited_by_count":12,"area":null},{"id":"2206.01693","title":"Three-dimensional microstructure generation using generative adversarial neural networks in the context of continuum micromechanics","created":"2022-05-31","cited_by_count":11,"area":null},{"id":"2210.03515","title":"Spiking neural networks for nonlinear regression","created":"2022-10-06","cited_by_count":10,"area":null},{"id":"2406.03172","title":"Initialization-enhanced Physics-Informed Neural Network with Domain Decomposition (IDPINN)","created":"2024-06-05","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":"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":"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":"2301.02378","title":"Deep learning for full-field ultrasonic characterization","created":"2023-01-06","cited_by_count":5,"area":null},{"id":"2303.03918","title":"Error convergence and engineering-guided hyperparameter search of PINNs: towards optimized I-FENN performance","created":"2023-03-03","cited_by_count":5,"area":null},{"id":"2309.11875","title":"Stochastic stiffness identification and response estimation of Timoshenko beams via physics-informed Gaussian processes","created":"2023-09-21","cited_by_count":5,"area":null},{"id":"2407.19707","title":"Neural networks for bifurcation and linear stability analysis of steady states in partial differential equations","created":"2024-07-29","cited_by_count":5,"area":null},{"id":"2212.07723","title":"Physics-Informed Neural Networks for Material Model Calibration from Full-Field Displacement Data","created":"2022-12-15","cited_by_count":4,"area":null},{"id":"2305.17387","title":"Learning from Integral Losses in Physics Informed Neural Networks","created":"2023-05-27","cited_by_count":4,"area":null},{"id":"2308.10038","title":"Physics-guided training of GAN to improve accuracy in airfoil design synthesis","created":"2023-08-19","cited_by_count":4,"area":null},{"id":"2306.01204","title":"Physics-informed UNets for Discovering Hidden Elasticity in Heterogeneous Materials","created":"2023-06-01","cited_by_count":3,"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":"2311.07798","title":"Probabilistic Physics-integrated Neural Differentiable Modeling for Isothermal Chemical Vapor Infiltration Process","created":"2023-11-13","cited_by_count":3,"area":null},{"id":"2502.08534","title":"Input convex neural networks: universal approximation theorem and implementation for isotropic polyconvex hyperelastic energies","created":"2025-02-12","cited_by_count":3,"area":null},{"id":"2504.07976","title":"EquiNO: A Physics-Informed Neural Operator for Multiscale Simulations","created":"2025-03-27","cited_by_count":3,"area":null},{"id":"2508.12063","title":"Generalized invariants meet constitutive neural networks: A novel framework for hyperelastic materials","created":"2025-08-16","cited_by_count":3,"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":"2509.07579","title":"Homogenization with Guaranteed Bounds via Primal-Dual Physically Informed Neural Networks","created":"2025-09-09","cited_by_count":2,"area":null},{"id":"2605.31231","title":"A holomorphic neural network framework for 3D boundary value problems governed by harmonic potentials","created":"2026-05-29","cited_by_count":2,"area":null},{"id":"2209.03151","title":"MRF-PINN: A Multi-Receptive-Field convolutional physics-informed neural network for solving partial differential equations","created":"2022-09-06","cited_by_count":1,"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":"2311.06147","title":"Incorporating sufficient physical information into artificial neural networks: a guaranteed improvement via physics-based Rao-Blackwellization","created":"2023-11-10","cited_by_count":1,"area":null},{"id":"2409.00994","title":"Physics-informed DeepONet with stiffness-based loss functions for structural response prediction","created":"2024-09-02","cited_by_count":1,"area":null},{"id":"2412.09453","title":"Finite-PINN: A Physics-Informed Neural Network with Finite Geometric Encoding for Solid Mechanics","created":"2024-12-12","cited_by_count":1,"area":null},{"id":"2505.07090","title":"Physics-informed Multiple-Input Operators for efficient dynamic response prediction of structures","created":"2025-05-11","cited_by_count":1,"area":null},{"id":"2506.16918","title":"A Neural Operator based Hybrid Microscale Model for Multiscale Simulation of Rate-Dependent Materials","created":"2025-06-20","cited_by_count":1,"area":null},{"id":"2303.09280","title":"Detecting hidden structures from a static loading experiment: topology optimization meets physics-informed neural networks","created":"2023-03-13","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":"2309.10418","title":"Graph Neural Networks for Dynamic Modeling of Roller Bearing","created":"2023-09-19","cited_by_count":0,"area":null},{"id":"2403.01776","title":"Hybrid data-driven and physics-informed regularized learning of cyclic plasticity with Neural Networks","created":"2024-03-04","cited_by_count":0,"area":null},{"id":"2405.13759","title":"Enhancing Multiscale Simulations with Constitutive Relations-Aware Deep Operator Networks","created":"2024-05-22","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":"2601.12704","title":"Adaptively trained Physics-informed Radial Basis Function Neural Networks for Solving Multi-asset Option Pricing Problems","created":"2026-01-19","cited_by_count":0,"area":null},{"id":"2603.15194","title":"PiGRAND: Physics-informed Graph Neural Diffusion for Intelligent Additive Manufacturing","created":"2026-03-16","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}