December 06, 2021 article Open access Predicting mechanically driven full-field quantities of interest with deep learning-based metamodels Extreme Mechanics Letters DOI: 10.1016/j.eml.2021.101566 Full text (OA) OpenAlex Authors 0 Author list not loaded yet. Cited by 3 stored of 42 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 Attention-Enhanced Damage Prediction in Electrode Materials From Simulation Video Sequences 2026 IEEE Transactions on Automation Science and Engineering article Engineering Advanced Battery Technologies Research Open access 0 citations 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 3 results References 0