{"total":24,"rows":[{"id":"2203.08205","title":"Learning Deep Implicit Fourier Neural Operators (IFNOs) with Applications to Heterogeneous Material Modeling","created":"2022-03-15","cited_by_count":65,"area":null},{"id":"2110.07374","title":"Physics informed neural networks for continuum micromechanics","created":"2021-10-14","cited_by_count":46,"area":null},{"id":"2405.03658","title":"A review on data-driven constitutive laws for solids","created":"2024-05-06","cited_by_count":35,"area":null},{"id":"2210.08343","title":"Modular machine learning-based elastoplasticity: generalization in the context of limited data","created":"2022-10-15","cited_by_count":16,"area":null},{"id":"2310.03652","title":"Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics","created":"2023-10-05","cited_by_count":16,"area":null},{"id":"2310.06872","title":"On sparse regression, Lp-regularization, and automated model discovery","created":"2023-10-09","cited_by_count":15,"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":"2305.03184","title":"A Generative Modeling Framework for Inferring Families of Biomechanical Constitutive Laws in Data-Sparse Regimes","created":"2023-05-04","cited_by_count":8,"area":null},{"id":"2208.09889","title":"G2{\\Phi}net: Relating Genotype and Biomechanical Phenotype of Tissues with Deep Learning","created":"2022-08-21","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":"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":"2502.05463","title":"Learning Memory and Material Dependent Constitutive Laws","created":"2025-02-08","cited_by_count":5,"area":null},{"id":"2503.00575","title":"Polyconvex Physics-Augmented Neural Network Constitutive Models in Principal Stretches","created":"2025-03-01","cited_by_count":5,"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":"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":"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":"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":"2305.08102","title":"A machine learning-based viscoelastic-viscoplastic model for epoxy nanocomposites with moisture content","created":"2023-05-14","cited_by_count":1,"area":null},{"id":"2506.17242","title":"Differentiable neural network representation of multi-well, locally-convex potentials","created":"2025-06-06","cited_by_count":1,"area":null},{"id":"2404.17584","title":"Equivariant graph convolutional neural networks for the representation of homogenized anisotropic microstructural mechanical response","created":"2024-04-05","cited_by_count":0,"area":null},{"id":"2506.08146","title":"Fully data-driven inverse hyperelasticity with hyper-network neural ODE fields","created":"2025-06-09","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":"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":"2607.20339","title":"Interval and fuzzy physics-augmented neural networks (iPANN and fPANN) for uncertainty quantification and propagation in constitutive modeling","created":"2026-07-22","cited_by_count":0,"area":null}],"next_cursor":null}