{"total":17,"rows":[{"id":"2407.19326","title":"Accounting for plasticity: An extension of inelastic Constitutive Artificial Neural Networks","created":"2024-07-27","cited_by_count":6,"area":null},{"id":"2410.13645","title":"Automated Model Discovery for Tensional Homeostasis: Constitutive Machine Learning in Growth and Remodeling","created":"2024-10-17","cited_by_count":6,"area":null},{"id":"2602.17750","title":"Inelastic Constitutive Kolmogorov-Arnold Networks: A generalized framework for automated discovery of interpretable inelastic material models","created":"2026-02-19","cited_by_count":6,"area":null},{"id":"2507.02991","title":"Physics Augmented Machine Learning Discovery of Composition-Dependent Constitutive Laws for 3D Printed Digital Materials","created":"2025-07-01","cited_by_count":4,"area":null},{"id":"2510.04187","title":"A Complement to Neural Networks for Anisotropic Inelasticity at Finite Strains","created":"2025-10-05","cited_by_count":4,"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":"2503.00268","title":"Input Specific Neural Networks","created":"2025-03-01","cited_by_count":2,"area":null},{"id":"2510.00884","title":"COMMET: orders-of-magnitude speed-up in finite element method via batch-vectorized neural constitutive updates","created":"2025-10-01","cited_by_count":2,"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":"2603.28707","title":"A Convex Route to Thermoelasticity: Learning Internal Energy and Dissipation","created":"2026-03-30","cited_by_count":1,"area":null},{"id":"2605.09058","title":"Nonlinear GENERIC-Embedded Neural Networks (N-GENNs): Learning GENERIC dynamics with non-quadratic dissipation potentials","created":"2026-05-09","cited_by_count":1,"area":null},{"id":"2403.18310","title":"A thermodynamically consistent physics-informed deep learning material model for short fiber/polymer nanocomposites","created":"2024-03-27","cited_by_count":0,"area":null},{"id":"2503.05419","title":"Physics-based machine learning for fatigue lifetime prediction under non-uniform loading scenarios","created":"2025-03-07","cited_by_count":0,"area":null},{"id":"2605.07738","title":"Physics-Informed Reduced-Order Operator Learning for Hyperelasticity in Continuum Micromechanics","created":"2026-05-08","cited_by_count":0,"area":null},{"id":"2605.31532","title":"Discovering Thermodynamically Admissible Dissipation Potentials via Grammar-Based Symbolic Regression","created":"2026-05-29","cited_by_count":0,"area":null},{"id":"2606.05199","title":"Automatic-Differentiation-Enabled Finite Element Model Updating: Learning Constitutive Neural Networks from Structural Deformation Data","created":"2026-05-22","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}