This page was last updated on 2024-07-17 08:46:35 UTC
Recommendations for the article Parsimony as the ultimate regularizer for physics-informed machine learning
Abstract | Title | Authors | Publication Date | Journal/ Conference | Citation count | Highest h-index |
---|---|---|---|---|---|---|
visibility_off | A Unified Sparse Optimization Framework to Learn Parsimonious Physics-Informed Models From Data | Kathleen P. Champion, P. Zheng, A. Aravkin, S. Brunton, J. Kutz | 2019-06-25 | IEEE Access | 94 | 63 |
visibility_off | Learning dynamical systems from data: An introduction to physics-guided deep learning | Rose Yu, Rui Wang | 2024-06-24 | Proceedings of the National Academy of Sciences of the United States of America | 1 | 1 |
visibility_off | Symbolic regression via neural networks. | N. Boddupalli, T. Matchen, J. Moehlis | 2023-08-01 | Chaos | 2 | 37 |
visibility_off | Machine Learning for Partial Differential Equations | S. Brunton, J. Kutz | 2023-03-30 | ArXiv | 14 | 63 |
visibility_off | Physics-Guided Deep Learning for Dynamical Systems: A survey | Rui Wang | 2021-07-02 | ArXiv | 46 | 10 |
visibility_off | Uncertainty and Structure in Neural Ordinary Differential Equations | Katharina Ott, Michael Tiemann, Philipp Hennig | 2023-05-22 | ArXiv | 3 | 38 |
visibility_off | Physics-informed learning of governing equations from scarce data | Zhao Chen, Yang Liu, Hao Sun | 2020-05-05 | Nature Communications | 230 | 12 |
visibility_off | Deep learning of physical laws from scarce data | Zhao Chen, Yang Liu, Hao Sun | 2020-05-05 | ArXiv | 19 | 12 |
visibility_off | Applying physics-based loss functions to neural networks for improved generalizability in mechanics problems | Samuel J. Raymond, David B. Camarillo | 2021-04-30 | ArXiv | 10 | 30 |
visibility_off | Physical laws meet machine intelligence: current developments and future directions | T. Muther, A. K. Dahaghi, F. I. Syed, Vuong Van Pham | 2022-12-05 | Artificial Intelligence Review | 17 | 16 |
Abstract | Title | Authors | Publication Date | Journal/Conference | Citation count | Highest h-index |