November 26, 2020 article Open access SciANN: A Keras/TensorFlow wrapper for scientific computations and physics-informed deep learning using artificial neural networks Computer Methods in Applied Mechanics and Engineering DOI: 10.1016/j.cma.2020.113552 Full text (OA) OpenAlex Authors 0 Author list not loaded yet. Cited by 18 stored of 429 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 Finite Operator Learning: Bridging neural operators and numerical methods for efficient parametric solution and optimization of PDEs 2025 Finite Elements in Analysis and Design article Physics and Astronomy Model Reduction and Neural Networks 10 citations A Finite Operator Learning Technique for Mapping the Elastic Properties of Microstructures to Their Mechanical Deformations 2024 International Journal for Numerical Methods in Engineering article Physics and Astronomy Model Reduction and Neural Networks Open access 27 citations Introducing a microstructure-embedded autoencoder approach for reconstructing high-resolution solution field data from a reduced parametric space 2024 Computational Mechanics article Physics and Astronomy Model Reduction and Neural Networks Open access 5 citations A finite element-based physics-informed operator learning framework for spatiotemporal partial differential equations on arbitrary domains 2024 Engineering With Computers article Physics and Astronomy Model Reduction and Neural Networks Open access 39 citations Learning solutions of thermodynamics-based nonlinear constitutive material models using physics-informed neural networks 2024 Computational Mechanics article Physics and Astronomy Model Reduction and Neural Networks 82 citations Perspectives of physics-based machine learning strategies for geoscientific applications governed by partial differential equations 2023 Geoscientific model development article Physics and Astronomy Model Reduction and Neural Networks Open access 39 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 Reply on RC2 2023 peer-review Physics and Astronomy Model Reduction and Neural Networks Open access 0 citations Comment on gmd-2022-309 2023 peer-review Physics and Astronomy Model Reduction and Neural Networks Open access 0 citations Physics-informed neural networks applied to catastrophic creeping landslides 2023 Landslides article Environmental Science Landslides and related hazards Open access 42 citations Reply on CEC1 2023 peer-review Physics and Astronomy Model Reduction and Neural Networks Open access 0 citations Reply on AC2 2023 peer-review Physics and Astronomy Model Reduction and Neural Networks Open access 0 citations Reply on RC1 2023 peer-review Physics and Astronomy Model Reduction and Neural Networks Open access 0 citations Comment on gmd-2022-309 2023 peer-review Physics and Astronomy Model Reduction and Neural Networks Open access 0 citations Perspectives of Physics-Based Machine Learning for Geoscientific Applications Governed by Partial Differential Equations 2023 preprint Physics and Astronomy Model Reduction and Neural Networks Open access 5 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 18 results References 0