July 14, 2026 article A novel physics-informed framework based on radial basis functions for solving forward and inverse elastostatic problems Acta Mechanica Sinica DOI: 10.1007/s10409-026-25967-x OpenAlex Authors 0 Author list not loaded yet. Cited by 0 stored of 0 References 1 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 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 1 result