March 12, 2021 article A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics Computer Methods in Applied Mechanics and Engineering DOI: 10.1016/j.cma.2021.113741 OpenAlex Authors 0 Author list not loaded yet. Cited by 26 stored of 1,327 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 Effects of variational formulations on physics‐informed neural network performance in solid mechanics 2023 PAMM article Physics and Astronomy Model Reduction and Neural Networks Open access 8 citations Physics-informed neural networks applied to catastrophic creeping landslides 2023 Landslides article Environmental Science Landslides and related hazards Open access 42 citations Spiking recurrent neural networks for neuromorphic computing in nonlinear structural mechanics 2023 Computer Methods in Applied Mechanics and Engineering article Physics and Astronomy Model Reduction and Neural Networks cited by 1 patent 23 citations Physics-Informed Neural Networks Applied to Catastrophic Creeping Landslides 2023 preprint Environmental Science Landslides and related hazards Open access 1 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 21–26 of 26 ← Previous Page 2 / 2 References 0