Research Interests
- AI for Science and Physics: neural operators, PDE-based modeling, operator learning
- Scientific ML for limited data and cross-resolution generalization
- Stochastic optimization: simulated annealing, RL-guided sampling and scheduling
Representative Topics
- Physics-informed operator learning and active data acquisition
- Cross-resolution learning and discretization robustness
- Hybrid optimization dynamics and quasi-equilibrium constraints
Publications
Merged from the former “Papers” page for convenience.
Journal & Conference
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Coordinate Transform Fourier Neural Operators for Symmetries in Physical Modelings
Transactions on Machine Learning Research (TMLR), 2024 • cites: 6
W. Gao, R. Xu, H. Wang, Y. Liu -
Discretization-Invariance? On the Discretization Mismatch Errors in Neural Operators
International Conference on Learning Representations (ICLR), 2025 • cites: 4
W. Gao, R. Xu, Y. Deng, Y. Liu -
Dynamic Schwartz-Fourier Neural Operator for Enhanced Expressive Power
Transactions on Machine Learning Research (TMLR), 2025 • cites: 1
W. Gao, J. Luo, R. Xu, Y. Liu
Preprints
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Velocity-Inferred Hamiltonian Neural Networks: Learning Energy-Conserving Dynamics from Position-Only Data
arXiv, 2025 • cites: 1
R. Xu, Z. Wu, L. Chen, G. Kementziadis, S. Wang, H. Wang, Y. Shi, Y. Deng -
An Iterative Framework for Generative Backmapping of Coarse-Grained Proteins
arXiv, 2025
G. Kementziadis, E. Wong, J. Nicholson, R. Xu, Y. Deng -
The Impact of Move Schemes on Simulated Annealing Performance
arXiv, 2025
R. Xu, H. Wang, Y. Deng
Workshops
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APOD: Adaptive PDE-Observation Diffusion for Physics-Constrained Sampling
ICML 2025 Workshop on Assessing World Models, 2025 (Workshop Website) (ICML Page)
R. Xu, H. Wang, G. Kementziadis, C. Si, Y. Deng -
RL-QESA: Reinforcement-Learning Quasi-Equilibrium Simulated Annealing
2nd AI for Math Workshop @ ICLR, 2025
R. Xu, K. Li, H. Wang, G. Kementziadis, W. Zhu, Y. Deng
In Preparation
- Moving Strategy of Simulated Annealing: Moving One Coordinate is All You Need, 2025.
- Reinforcement Learning-Guided Simulated Annealing: Adaptive Temperature Scheduling via Quasi-Equilibrium Constraints, 2025.
- Physics-Informed Active Learning for Neural Operators, 2025.