May 21, 2019 article Open access Machine learning materials physics: Integrable deep neural networks enable scale bridging by learning free energy functions Computer Methods in Applied Mechanics and Engineering DOI: 10.1016/j.cma.2019.05.019 Full text (OA) OpenAlex Authors 0 Author list not loaded yet. Cited by 4 stored of 111 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 Gibbs-Duhem-Informed Neural Networks for Binary Activity Coefficient Prediction 2023 arXiv (Cornell University) preprint Engineering Phase Equilibria and Thermodynamics Open access 1 citations Gibbs–Duhem-informed neural networks for binary activity coefficient prediction 2023 Digital Discovery article Engineering Phase Equilibria and Thermodynamics Open access 60 citations Explainable Artificial Intelligence for Mechanics: Physics-Explaining Neural Networks for Constitutive Models 2022 Frontiers in Materials article Physics and Astronomy Model Reduction and Neural Networks Open access 41 citations An intelligent nonlinear meta element for elastoplastic continua: deep learning using a new Time-distributed Residual U-Net architecture 2020 Computer Methods in Applied Mechanics and Engineering article Physics and Astronomy Model Reduction and Neural Networks 71 citations 4 results References 0