May 21, 2021 article Physics-Informed Deep Learning for Computational Elastodynamics without Labeled Data Journal of Engineering Mechanics DOI: 10.1061/(asce)em.1943-7889.0001947 OpenAlex Authors 0 Author list not loaded yet. Cited by 7 stored of 336 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 A new set of test functions for variational physics-informed neural networks in solid mechanics 2026 Journal of Computational Physics article Physics and Astronomy Model Reduction and Neural Networks Open access 0 citations SPiFOL: A Spectral-based physics-informed finite operator learning for prediction of mechanical behavior of microstructures 2025 Journal of the Mechanics and Physics of Solids article Physics and Astronomy Model Reduction and Neural Networks Open access 18 citations 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 On the order of derivation in the training of physics-informed neural networks: case studies for non-uniform beam structures 2023 Acta Mechanica article Physics and Astronomy Model Reduction and Neural Networks 25 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 7 results References 0