| Physics-informed machine learning: case studies for weather and climate modelling K Kashinath, M Mustafa, A Albert, JL Wu, C Jiang, S Esmaeilzadeh, ... Philosophical Transactions of the Royal Society A 379 (2194), 20200093, 2021 | 391 | 2021 |
| Towards physics-informed deep learning for turbulent flow prediction R Wang, K Kashinath, M Mustafa, A Albert, R Yu Proceedings of the 26th ACM SIGKDD International Conference on Knowledge …, 2020 | 370 | 2020 |
| Incorporating symmetry into deep dynamics models for improved generalization R Wang, R Walters, R Yu International Conference on Learning Representations (ICLR), 2021 | 169 | 2021 |
| Physics-guided deep learning for dynamical systems: A survey R Wang, R Yu arXiv preprint arXiv:2107.01272, 2021 | 75 | 2021 |
| Artificial intelligence for science in quantum, atomistic, and continuum systems X Zhang, L Wang, J Helwig, Y Luo, C Fu, Y Xie, M Liu, Y Lin, Z Xu, K Yan, ... arXiv preprint arXiv:2307.08423, 2023 | 71 | 2023 |
| Approximately Equivariant Networks for Imperfectly Symmetric Dynamics R Wang, R Walters, R Yu International Conference on Machine Learning (ICML), 2022 | 64 | 2022 |
| Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems R Wang, D Maddix, C Faloutsos, Y Wang, R Yu Learning for Dynamics and Control, 385-398, 2021 | 54 | 2021 |
| Prediction of Alzheimer’s disease-associated genes by integration of GWAS summary data and expression data S Hao, R Wang, Y Zhang, H Zhan Frontiers in genetics 9, 653, 2019 | 40 | 2019 |
| Meta-learning dynamics forecasting using task inference R Wang, R Walters, R Yu Advances in Neural Information Processing Systems 35, 21640-21653, 2022 | 27 | 2022 |
| Koopman Neural Operator Forecaster for Time-series with Temporal Distributional Shifts R Wang, Y Dong, SO Arik, R Yu The Eleventh International Conference on Learning Representations, 2023 | 24* | 2023 |
| Data Augmentation vs. Equivariant Networks: A Theory of Generalization on Dynamics Forecasting R Wang, R Walters, R Yu International Conference on Machine Learning (ICML) Principles of …, 2022 | 14 | 2022 |
| Aortic Pressure Forecasting with Deep Learning E Huang, R Wang, U Chandrasekaran, R Yu 2020 Computing in Cardiology, 2020 | 6* | 2020 |
| Learning Dynamical Systems Requires Rethinking Generalization R Wang, D Maddix, C Faloutsos, Y Wang, R Yu | 4 | 2020 |
| Relaxed Octahedral Group Convolution for Learning Symmetry Breaking in 3D Physical Systems R Wang, R Walters, TE Smidt arXiv preprint arXiv:2310.02299, 2023 | 2 | 2023 |
| Learning dynamical systems from data: An introduction to physics-guided deep learning R Yu, R Wang Proceedings of the National Academy of Sciences 121 (27), e2311808121, 2024 | 1 | 2024 |
| Discovering Symmetry Breaking in Physical Systems with Relaxed Group Convolution R Wang, E Hofgard, H Gao, R Walters, T Smidt Forty-first International Conference on Machine Learning, 2024 | 1 | 2024 |
| Intra-aortic pressure forecasting A El Katerji, E Kroeker, E Jortberg, R Yu, R Wang US Patent 11,581,083, 2023 | 1 | 2023 |
| Physics-guided deep learning for spatiotemporal forecasting R Wang, R Walters, R Yu Knowledge Guided Machine Learning, 179-210, 2022 | 1 | 2022 |
| AutoODE: Bridging Physics-based and Data-driven modeling for COVID-19 Forecasting R Wang, D Maddix, C Faloutsos, Y Wang, R Yu | 1 | 2020 |
| A Recipe for Charge Density Prediction X Fu, A Rosen, K Bystrom, R Wang, A Musaelian, B Kozinsky, T Smidt, ... arXiv preprint arXiv:2405.19276, 2024 | | 2024 |
Rose YuAssociate Professor, University of California, San DiegoVerified email at ucsd.edu
