Rui Wang

According to our database, Rui Wang authored at least 16 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Relaxed Equivariant Graph Neural Networks.

Elyssa F. Hofgard, Rui Wang, Robin Walters, Tess E. Smidt

CoRR, 2024

A Recipe for Charge Density Prediction.

Xiang Fu, Andrew S. Rosen, Kyle Bystrom, Rui Wang, Albert Musaelian, Boris Kozinsky, Tess E. Smidt, Tommi S. Jaakkola

CoRR, 2024

Discovering Symmetry Breaking in Physical Systems with Relaxed Group Convolution.

Rui Wang, Elyssa F. Hofgard, Hang Gao, Robin Walters, Tess E. Smidt

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Physics-Guided Deep Learning for Dynamics Forecasting

Rui Wang

PhD thesis, 2023

Relaxed Octahedral Group Convolution for Learning Symmetry Breaking in 3D Physical Systems.

Rui Wang, Robin Walters, Tess E. Smidt

CoRR, 2023

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems.

Xuan Zhang, Limei Wang, Jacob Helwig, Youzhi Luo, Cong Fu, Yaochen Xie, Meng Liu, Yuchao Lin, Zhao Xu, Keqiang Yan, Keir Adams, Maurice Weiler, Xiner Li, Tianfan Fu, Yucheng Wang, Haiyang Yu, Yuqing Xie, Xiang Fu, Alex Strasser, Shenglong Xu, Yi Liu, Yuanqi Du, Alexandra Saxton, Hongyi Ling, Hannah Lawrence, Hannes Stärk, Shurui Gui, Carl Edwards, Nicholas Gao, Adriana Ladera, Tailin Wu, Elyssa F. Hofgard, Aria Mansouri Tehrani, Rui Wang, Ameya Daigavane, Montgomery Bohde, Jerry Kurtin, Qian Huang, Tuong Phung, Minkai Xu, Chaitanya K. Joshi, Simon V. Mathis, Kamyar Azizzadenesheli, Ada Fang, Alán Aspuru-Guzik, Erik J. Bekkers, Michael M. Bronstein, Marinka Zitnik, Anima Anandkumar, Stefano Ermon, Pietro Liò, Rose Yu, Stephan Günnemann, Jure Leskovec, Heng Ji, Jimeng Sun, Regina Barzilay, Tommi S. Jaakkola, Connor W. Coley, Xiaoning Qian, Xiaofeng Qian, Tess E. Smidt, Shuiwang Ji

CoRR, 2023

Koopman Neural Operator Forecaster for Time-series with Temporal Distributional Shifts.

Rui Wang, Yihe Dong, Sercan Ö. Arik, Rose Yu

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts.

Rui Wang, Yihe Dong, Sercan Ö. Arik, Rose Yu

CoRR, 2022

Data Augmentation vs. Equivariant Networks: A Theory of Generalization on Dynamics Forecasting.

Rui Wang, Robin Walters, Rose Yu

CoRR, 2022

Meta-Learning Dynamics Forecasting Using Task Inference.

Rui Wang, Robin Walters, Rose Yu

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Approximately Equivariant Networks for Imperfectly Symmetric Dynamics.

Rui Wang, Robin Walters, Rose Yu

Proceedings of the International Conference on Machine Learning, 2022

2021

Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems.

Rui Wang, Danielle C. Maddix, Christos Faloutsos, Yuyang Wang, Rose Yu

Proceedings of the 3rd Annual Conference on Learning for Dynamics and Control, 2021

Incorporating Symmetry into Deep Dynamics Models for Improved Generalization.

Rui Wang, Robin Walters, Rose Yu

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Aortic Pressure Forecasting with Deep Sequence Learning.

Rui Wang, Eliza Huang, Uma Chandrasekaran, Rose Yu

CoRR, 2020

Towards Physics-informed Deep Learning for Turbulent Flow Prediction.

Rui Wang, Karthik Kashinath, Mustafa Mustafa, Adrian Albert, Rose Yu

Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

Aortic Pressure Forecasting With Deep Learning.

Eliza Huang, Rui Wang, Uma Chandrasekaran, Rose Yu

Proceedings of the Computing in Cardiology, 2020