May 05, 2026 article Open access Uncertainty quantification in model discovery by distilling interpretable material constitutive models from Gaussian process posteriors Computer Methods in Applied Mechanics and Engineering DOI: 10.1016/j.cma.2026.118979 Full text (OA) OpenAlex Authors 0 Author list not loaded yet. Cited by 0 stored of 1 References 4 Search Sort Most cited Newest Oldest Patent citations Title Any typearticle review book-chapter conference-paper preprint dissertation book dataset other Any fieldAgricultural and Biological Sciences Arts and Humanities Biochemistry, Genetics and Molecular Biology Business, Management and Accounting Chemical Engineering Chemistry Computer Science Decision Sciences Dentistry Earth and Planetary Sciences Economics, Econometrics and Finance Energy Engineering Environmental Science Health Professions Immunology and Microbiology Materials Science Mathematics Medicine Neuroscience Nursing Pharmacology, Toxicology and Pharmaceutics Physics and Astronomy Psychology Social Sciences Veterinary Open access Constitutive artificial neural networks: A fast and general approach to predictive data-driven constitutive modeling by deep learning 2020 Journal of Computational Physics article Materials Science Machine Learning in Materials Science Open access cited by 3 patents 341 citations Polyconvex anisotropic hyperelasticity with neural networks 2021 Journal of the Mechanics and Physics of Solids article Engineering Elasticity and Material Modeling Open access 238 citations Automated model discovery for human cardiac tissue: Discovering the best model and parameters 2024 Computer Methods in Applied Mechanics and Engineering article Engineering Elasticity and Material Modeling Open access 46 citations Discovering uncertainty: Bayesian constitutive artificial neural networks 2024 Computer Methods in Applied Mechanics and Engineering article Computer Science Gaussian Processes and Bayesian Inference Open access 37 citations 4 results