{"total":21,"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":"2205.06664","title":"NN-EUCLID: deep-learning hyperelasticity without stress data","created":"2022-05-04","cited_by_count":30,"area":null},{"id":"2309.15421","title":"Deep Learning in Deterministic Computational Mechanics","created":"2023-09-27","cited_by_count":21,"area":null},{"id":"2503.05617","title":"Can KAN CANs? Input-convex Kolmogorov-Arnold Networks (KANs) as hyperelastic constitutive artificial neural networks (CANs)","created":"2025-03-07","cited_by_count":13,"area":null},{"id":"2203.16577","title":"Calibrating constitutive models with full-field data via physics informed neural networks","created":"2022-03-30","cited_by_count":12,"area":null},{"id":"2308.11080","title":"Stress representations for tensor basis neural networks: alternative formulations to Finger-Rivlin-Ericksen","created":"2023-08-21","cited_by_count":9,"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":"2306.15873","title":"Discovering stochastic partial differential equations from limited data using variational Bayes inference","created":"2023-06-28","cited_by_count":6,"area":null},{"id":"2509.16040","title":"Automated Constitutive Model Discovery by Pairing Sparse Regression Algorithms with Model Selection Criteria","created":"2025-09-19","cited_by_count":3,"area":null},{"id":"2604.07746","title":"Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU)","created":"2026-04-09","cited_by_count":3,"area":null},{"id":"2212.00881","title":"Investigating Deep Learning Model Calibration for Classification Problems in Mechanics","created":"2022-12-01","cited_by_count":2,"area":null},{"id":"2510.09498","title":"Unsupervised full-field Bayesian inference of orthotropic hyperelasticity from a single biaxial test: a myocardial case study","created":"2025-10-10","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":"2503.12679","title":"Discovering uncertainty: Gaussian constitutive neural networks with correlated weights","created":"2025-03-16","cited_by_count":1,"area":null},{"id":"2407.20273","title":"Learning Physics-Consistent Material Behavior from Dynamic Displacements","created":"2024-07-25","cited_by_count":0,"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":"2502.07415","title":"The Illusion of Fit: Spatially Resolved Assessment of Constitutive Model Validity in Elastography and Physics-Based Inverse Problems","created":"2025-02-11","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":"2606.14565","title":"CANN-EUCLID: unsupervised constitutive artificial neural network model discovery from full-field data","created":"2026-06-12","cited_by_count":0,"area":null},{"id":"2606.19375","title":"Physics-Informed Discovery of Yield Functions in Plasticity via Convex Neural Representations","created":"2026-06-12","cited_by_count":0,"area":null},{"id":"2608.12624","title":"Structure-preserving uncertainty quantification for GENERIC dynamics","created":"2026-08-12","cited_by_count":0,"area":null}],"next_cursor":null}