6 papers
Hybrid Lagrangian-Eulerian Model for Lagrangian Fluid Simulation
Ruoyan Li, Wei Wang, Yizhou Sun
Pure Lagrangian neural simulators offer geometric flexibility and exact advection, making them well-suited for modeling moving domains and free surfaces. However, the absence of a…
Generalized Neural Operator for Parametric and Boundary-Value Problems
Ruoyan Li, Yizhou Sun, Wei Wang
Developing foundational neural simulators for Partial Differential Equations (PDEs) requires robust generalization across diverse physical parameters and boundary conditions. Howev…
ARLArena: A Unified Framework for Stable Agentic Reinforcement Learning
Xiaoxuan Wang, Han Zhang, Haixin Wang +11
Agentic reinforcement learning (ARL) has rapidly gained attention as a promising paradigm for training agents to solve complex, multi-step interactive tasks. Despite encouraging ea…
FD-Bench: A Modular and Fair Benchmark for Data-driven Fluid Simulation
Haixin Wang, Ruoyan Li, Fred Xu +7
Data-driven modeling of fluid dynamics has advanced rapidly with neural PDE solvers, yet a fair and strong benchmark remains fragmented due to the absence of unified PDE datasets a…
Flow Field Reconstruction with Sensor Placement Policy Learning
Ruoyan Li, Guancheng Wan, Zijie Huang +5
Flow-field reconstruction from sparse sensor measurements remains a central challenge in modern fluid dynamics, as the need for high-fidelity data often conflicts with practical li…
Self-Guided Diffusion Model for Accelerating Computational Fluid Dynamics
Ruoyan Li, Zijie Huang, Haixin Wang +3
Machine learning methods, such as diffusion models, are widely explored as a promising way to accelerate high-fidelity fluid dynamics computation via a super-resolution process fro…