April 09, 2021 article Open access Deep learning model to predict complex stress and strain fields in hierarchical composites Science Advances DOI: 10.1126/sciadv.abd7416 Full text (OA) OpenAlex Authors 0 Author list not loaded yet. Cited by 6 stored of 399 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 Digitalizing metallic materials from image segmentation to multiscale solutions via physics informed operator learning 2025 npj Computational Materials article Materials Science Machine Learning in Materials Science Open access 7 citations A spatiotemporal deep learning framework for prediction of crack dynamics in heterogeneous solids: Efficient mapping of concrete microstructures to its fracture properties 2024 Engineering Fracture Mechanics article Engineering Numerical methods in engineering 18 citations Mixed formulation of physics‐informed neural networks for thermo‐mechanically coupled systems and heterogeneous domains 2023 International Journal for Numerical Methods in Engineering article Physics and Astronomy Model Reduction and Neural Networks Open access 98 citations An artificial neural network for surrogate modeling of stress fields in viscoplastic polycrystalline materials 2023 npj Computational Materials article Materials Science Machine Learning in Materials Science Open access 97 citations A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: Comparison with finite element method 2022 Computer Methods in Applied Mechanics and Engineering article Physics and Astronomy Model Reduction and Neural Networks Open access 12 citations A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: comparison with finite element method 2022 arXiv (Cornell University) preprint Physics and Astronomy Model Reduction and Neural Networks Open access 216 citations 6 results References 0