| PROSE: Predicting Multiple Operators and Symbolic Expressions using multimodal transformers Y Liu, Z Zhang, H Schaeffer Neural Networks 180, 106707, 2024 | 16 | 2024 |
| Recent advances on machine learning for computational fluid dynamics: A survey H Wang, Y Cao, Z Huang, Y Liu, P Hu, X Luo, Z Song, W Zhao, J Liu, ... arXiv preprint arXiv:2408.12171, 2024 | 16 | 2024 |
| Towards a Foundation Model for Partial Differential Equations: Multi-Operator Learning and Extrapolation J Sun, Y Liu, Z Zhang, H Schaeffer arXiv preprint arXiv:2404.12355, 2024 | 16 | 2024 |
| Random feature models for learning interacting dynamical systems Y Liu, SG McCalla, H Schaeffer Proceedings of the Royal Society A 479 (2275), 20220835, 2023 | 8 | 2023 |
| Prose-fd: A multimodal pde foundation model for learning multiple operators for forecasting fluid dynamics Y Liu, J Sun, X He, G Pinney, Z Zhang, H Schaeffer arXiv preprint arXiv:2409.09811, 2024 | 4 | 2024 |
| VICON: Vision In-Context Operator Networks for Multi-Physics Fluid Dynamics Prediction Y Cao, Y Liu, L Yang, R Yu, H Schaeffer, S Osher arXiv preprint arXiv:2411.16063, 2024 | 1 | 2024 |
| A Multimodal PDE Foundation Model for Prediction and Scientific Text Descriptions E Negrini, Y Liu, L Yang, SJ Osher, H Schaeffer arXiv preprint arXiv:2502.06026, 2025 | | 2025 |
| BCAT: A Block Causal Transformer for PDE Foundation Models for Fluid Dynamics Y Liu, J Sun, H Schaeffer arXiv preprint arXiv:2501.18972, 2025 | | 2025 |
| SURFACTANT DYNAMICS FROM THE ARNOLD PERSPECTIVE J JENKINS, C LEE, Y LIU, E LU, D REED | | 2021 |
Hayden SchaefferProfessor of Mathematics, UCLAZweryfikowany adres z ucla.edu
