{"total":17,"rows":[{"id":"2405.03658","title":"A review on data-driven constitutive laws for solids","created":"2024-05-06","cited_by_count":35,"area":null},{"id":"2308.12716","title":"Solving Forward and Inverse Problems of Contact Mechanics using Physics-Informed Neural Networks","created":"2023-08-24","cited_by_count":18,"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":"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":"2404.00074","title":"A finite operator learning technique for mapping the elastic properties of microstructures to their mechanical deformations","created":"2024-03-28","cited_by_count":10,"area":null},{"id":"2311.00578","title":"Transfer learning for improved generalizability in causal physics-informed neural networks for beam simulations","created":"2023-11-01","cited_by_count":8,"area":null},{"id":"2304.06044","title":"Learning solution of nonlinear constitutive material models using physics-informed neural networks: COMM-PINN","created":"2023-04-10","cited_by_count":6,"area":null},{"id":"2306.12006","title":"Learning Homogenization for Elliptic Operators","created":"2023-06-21","cited_by_count":6,"area":null},{"id":"2502.05463","title":"Learning Memory and Material Dependent Constitutive Laws","created":"2025-02-08","cited_by_count":5,"area":null},{"id":"2506.10352","title":"History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials","created":"2025-06-12","cited_by_count":5,"area":null},{"id":"2505.01060","title":"Monotone Peridynamic Neural Operator for Nonlinear Material Modeling with Conditionally Unique Solutions","created":"2025-05-02","cited_by_count":3,"area":null},{"id":"2511.09906","title":"Beyond empirical models: Discovering new constitutive laws in solids with graph-based equation discovery","created":"2025-11-13","cited_by_count":2,"area":null},{"id":"2308.03915","title":"Predicting and explaining nonlinear material response using deep Physically Guided Neural Networks with Internal Variables","created":"2023-08-07","cited_by_count":1,"area":null},{"id":"2501.00556","title":"Finding the Underlying Viscoelastic Constitutive Equation via Universal Differential Equations and Differentiable Physics","created":"2024-12-31","cited_by_count":0,"area":null},{"id":"2601.03367","title":"Physics-Informed Gaussian Process Regression for the Constitutive Modeling of Concrete: A Data-Driven Improvement to Phenomenological Models","created":"2026-01-06","cited_by_count":0,"area":null},{"id":"2608.08114","title":"Finite basis physics-informed neural networks with hard constraints for viscous fluid flow in highly perforated domains","created":"2026-08-08","cited_by_count":0,"area":null},{"id":"2609.02194","title":"Learning the Constitutive Behavior of Materials via Neural Operators and Causal Attention: Case Studies in Plasticity and Damage","created":"2026-09-02","cited_by_count":0,"area":null}],"next_cursor":null}