4 papers · 1 filter
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…
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…