| A review of artificial neural networks in the constitutive modeling of composite materials X Liu, S Tian, F Tao, W Yu Composites Part B: Engineering 224, 109152, 2021 | 210 | 2021 |
| A neural network enhanced system for learning nonlinear constitutive law and failure initiation criterion of composites using indirectly measurable data X Liu, F Tao, W Yu Composite Structures 252, 112658, 2020 | 49 | 2020 |
| Learning nonlinear constitutive laws using neural network models based on indirectly measurable data X Liu, F Tao, H Du, W Yu, K Xu Journal of Applied Mechanics 87 (8), 081003, 2020 | 40 | 2020 |
| Learning composite constitutive laws via coupling Abaqus and deep neural network F Tao, X Liu, H Du, W Yu Composite Structures 272, 114137, 2021 | 32 | 2021 |
| Physics-informed artificial neural network approach for axial compression buckling analysis of thin-walled cylinder F Tao, X Liu, H Du, W Yu AIAA Journal 58 (6), 2737-2747, 2020 | 26 | 2020 |
| Finite element coupled positive definite deep neural networks mechanics system for constitutive modeling of composites F Tao, X Liu, H Du, W Yu Computer Methods in Applied Mechanics and Engineering 391, 114548, 2022 | 16 | 2022 |
| Crystallographic effects on transgranular chloride-induced stress corrosion crack propagation of arc welded austenitic stainless steel HJ Qu, F Tao, N Gu, T Montoya, JM Taylor, RF Schaller, E Schindelholz, ... Npj Materials Degradation 6 (1), 43, 2022 | 13 | 2022 |
| Multiscale analysis of multilayer printed circuit board using mechanics of structure genome F Tao, X Lyu, X Liu, W Yu Mechanics of Advanced Materials and Structures 28 (8), 774-783, 2021 | 12 | 2021 |
| How machine learning can help the design and analysis of composite materials and structures? X Liu, S Tian, F Tao, H Du, W Yu arXiv preprint arXiv:2010.09438, 2020 | 11 | 2020 |
| Discover failure criteria of composites from experimental data by sparse regression F Tao, X Liu, H Du, S Tian, W Yu Composites Part B: Engineering 239, 109947, 2022 | 10 | 2022 |
| Machine learning-assisted modeling of composite materials and structures: a review X Liu, S Tian, F Tao, H Du, W Yu AIAA Scitech 2021 Forum, 2023, 2021 | 7 | 2021 |
| Structural design optimization of composite rotor blades with strength considerations S Tian, F Tao, H Du, W Yu, JW Lim, RB Haehnel, Y Wenren, LD Allen AIAA SciTech 2022 Forum, 2454, 2022 | 6 | 2022 |
| A neural network enhanced system for learning nonlinear constitutive relation of fiber reinforced composites X Liu, F Tao, W Yu AIAA Scitech 2020 Forum, 0396, 2020 | 6 | 2020 |
| Effect of temperature on deep lined circular tunnels in transversely anisotropic elastic rock F Tao, A Bobet Underground Space 1 (2), 79-93, 2016 | 6 | 2016 |
| Learning damage constitutive law of composites via lamination theory enhanced Abaqus-PDNN mechanics system F Tao, X Liu, H Du, W Yu AIAA Scitech 2021 Forum, 2022, 2021 | 1 | 2021 |
| Data: Physics-informed artificial neural network approach for axial compression buckling analysis of thin-walled cylinder F Tao | 1 | 2019 |
| Ivabs: A New Design Tool for Composite Slender Structures. Tian, Su and Du, Haodong and Tao, Fei and Yu, Wenbin and Lim, Joon W. and ... SSRN, 2024 | | 2024 |
| Multiscale modeling of quasi-static crushing behavior of body-centered cubic lattices with I-shape beams and reinforced joints X Liu, T Kothari, F Tao, MH Kobir, Y Yang AIAA SCITECH 2024 Forum, 2290, 2024 | | 2024 |
| Machine Learning Assisted Composite Rotor Blade Planform Design F Tao, S Tian, H Du, W Yu ASME Aerospace Structures, Structural Dynamics, and Materials Conference …, 2023 | | 2023 |
| Physics-based data-driven modeling of composite materials and structures through machine learning F Tao Purdue University, 2022 | | 2022 |
Wenbin YuPurdue UniversityBestätigte E-Mail-Adresse bei purdue.edu
