most citedEfficient Learning of Mesh-Based Physical Simulation with BSMS-GNN

5 citations · 5 across the 4 of their papers we have counts for

collaborators

9 papers

cs.LG20265 cited

Efficient Learning of Mesh-Based Physical Simulation with BSMS-GNN

Yadi Cao, Menglei Chai, Minchen Li +1

Learning the physical simulation on large-scale meshes with flat Graph Neural Networks (GNNs) and stacking Message Passings (MPs) is challenging due to the scaling complexity w.r.t…

cs.GR2026

YASPS: A Symbolic Framework for Extensible, High-Performance IPC Simulation

Xuan Tang, Kemeng Huang, Gilbert Bernstein +2

Incremental Potential Contact (IPC) enables robust, contact-rich simulation by casting elasticity and contact as a single energy minimization problem, but high-performance IPC pipe…

cs.CE2026

An Overlapping Schwarz Space-Time Refinement Framework for Material Point Method

Zhaofeng Luo, Minchen Li, Yupeng Jiang

We propose an overlapping Schwarz space-time refinement framework for the material point method (OS-MPM) to improve computational efficiency in problems with strongly localized def…

cs.GR2026

Penetration-free Solid-Fluid Interaction on Shells and Rods

Yuchen Sun, Jinyuan Liu, Yin Yang +3

We introduce a novel approach to simulate the interaction between fluids and thin elastic solids without any penetration. Our approach is centered around an optimization system aug…

cs.CE2026

An Implicit Compact-Kernel Material Point Method for Computational Solid Mechanics

Qirui Fu, Yupeng Jiang, Minchen Li

The numerical performance of the material point method (MPM) is strongly governed by the particle-grid kernel, which controls the trade-off among smoothness, locality, numerical di…

cs.GR2026

Energy-Controllable Time Integration for Elastodynamic Contact

Kevin You, Juntian Zheng, Minchen Li

Dynamic simulation of elastic bodies is a longstanding task in engineering and computer graphics. In graphics, numerical integrators like implicit Euler and BDF2 are preferred due…