| CENN: Conservative energy method based on neural networks with subdomains for solving variational problems involving heterogeneous and complex geometries Y Wang, J Sun, W Li, Z Lu, Y Liu Computer Methods in Applied Mechanics and Engineering 400, 115491, 2022 | 17 | 2022 |
| Multi-scale modeling in thermal conductivity of Polyurethane incorporated with Phase Change Materials using Physics-Informed Neural Networks B Liu, Y Wang, T Rabczuk, T Olofsson, W Lu Renewable Energy 220, 119565, 2024 | 13 | 2024 |
| BINN: A deep learning approach for computational mechanics problems based on boundary integral equations J Sun, Y Liu, Y Wang, Z Yao, X Zheng Computer Methods in Applied Mechanics and Engineering 410, 116012, 2023 | 11 | 2023 |
| Kolmogorov Arnold Informed neural network: A physics-informed deep learning framework for solving PDEs based on Kolmogorov Arnold Networks Y Wang, J Sun, J Bai, C Anitescu, MS Eshaghi, X Zhuang, T Rabczuk, ... arXiv preprint arXiv:2406.11045, 2024 | 3 | 2024 |
| A deep complementary energy method for solid mechanics using minimum complementary energy principle Y Wang, J Sun, T Rabczuk, Y Liu arXiv preprint arXiv:2302.01538, 2023 | 2 | 2023 |
| PETS-SWINF: A regression method that considers images with metadata based Neural Network for pawpularity prediction on 2021 Kaggle Competition" PetFinder. my" Y Wang, Y Liu arXiv preprint arXiv:2201.06061, 2022 | 1 | 2022 |
| A robust radial point interpolation method empowered with neural network solvers (RPIM-NNS) for nonlinear solid mechanics J Bai, GR Liu, T Rabczuk, Y Wang, XQ Feng, YT Gu Computer Methods in Applied Mechanics and Engineering 429, 117159, 2024 | | 2024 |
| Deepnetbeam: A Framework for the Analysis of Functionally Graded Porous Beams MS Es-haghi, M Bamdad, C Anitescu, Y Wang, X Zhuang, T Rabczuk Available at SSRN 4846935, 2024 | | 2024 |
| HomoGenius: a Foundation Model of Homogenization for Rapid Prediction of Effective Mechanical Properties using Neural Operators Y Wang, X Li, Z Yan, Y Du, J Bai, B Liu, T Rabczuk | | 2024 |
| Deep Learning-Based Multi-Scale Modeling of Thermal Conductivity in Polyurethane with Phase Change Materials Via Neural Operators B Liu, Y Wang, Y Liu, K Feng, T Rabczuk, T Olofsson, J Bai, W Lu Available at SSRN 4702962, 2024 | | 2024 |
Timon RabczukBauhaus University Weimar在 uni-weimar.de 的电子邮件经过验证
