117 citations · 637 across the 31 of their papers we have counts for
31 papers
Unified sparse framework for large-scale simulations using the material point method
Yidong Zhao, Lars Blatny, Xiang Feng +3
The material point method (MPM) is a hybrid particle-grid method widely used for large deformation problems with history-dependent behavior, including geophysical mass flows. Stand…
Adaptive GPU Kinetic Solver for Fluid-Granular Flows
Xingqiao Li, Kui Wu, Haozhe Su +5
Simulating fluid-granular flows is crucial for understanding natural disasters, industrial processes, and visually realistic phenomena in computer graphics. These systems are chall…
Substepping the Material Point Method
Chenfanfu Jiang
Many Material Point Method implementations favor explicit time integration. However large time steps are often desirable for special reasons - for example, for partitioned coupling…
Neural Stress Fields for Reduced-order Elastoplasticity and Fracture
Zeshun Zong, Xuan Li, Minchen Li +6
We propose a hybrid neural network and physics framework for reduced-order modeling of elastoplasticity and fracture. State-of-the-art scientific computing models like the Material…
Convergent Incremental Potential Contact
Minchen Li, Zachary Ferguson, Teseo Schneider +5
Recent advances in the simulation of frictionally contacting elastodynamics with the Incremental Potential Contact (IPC) model have enabled inversion and intersection-free simulati…
PAC-NeRF: Physics Augmented Continuum Neural Radiance Fields for Geometry-Agnostic System Identification
Xuan Li, Yi-Ling Qiao, Peter Yichen Chen +4
Existing approaches to system identification (estimating the physical parameters of an object) from videos assume known object geometries. This precludes their applicability in a v…
A Contact Proxy Splitting Method for Lagrangian Solid-Fluid Coupling
Tianyi Xie, Minchen Li, Yin Yang +1
We present a robust and efficient method for simulating Lagrangian solid-fluid coupling based on a new operator splitting strategy. We use variational formulations to approximate f…
TPA-Net: Generate A Dataset for Text to Physics-based Animation
Yuxing Qiu, Feng Gao, Minchen Li +3
Recent breakthroughs in Vision-Language (V&L) joint research have achieved remarkable results in various text-driven tasks. High-quality Text-to-video (T2V), a task that has been l…
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…
Midas: A Multi-Joint Robotics Simulator with Intersection-Free Frictional Contact
Yunuo Chen, Minchen Li, Wenlong Lu +2
We introduce Midas, a robotics simulation framework based on the Incremental Potential Contact (IPC) model. Our simulator guarantees intersection-free, stable, and accurate resolut…
Circumventing volumetric locking in explicit material point methods: A simple, efficient, and general approach
Yidong Zhao, Chenfanfu Jiang, Jinhyun Choo
The material point method (MPM) is frequently used to simulate large deformations of nearly incompressible materials such as water, rubber, and undrained porous media. However, MPM…
Topology Optimization with Frictional Self-Contact
Zeshun Zong, Xuan Li, Jianping Ye +5
Contact-aware topology optimization faces challenges in robustness, accuracy, and applicability to internal structural surfaces under self-contact. This work builds on the recently…
Complex Locomotion Skill Learning via Differentiable Physics
Yu Fang, Jiancheng Liu, Mingrui Zhang +6
Differentiable physics enables efficient gradient-based optimizations of neural network (NN) controllers. However, existing work typically only delivers NN controllers with limited…
Affine Body Dynamics: Fast, Stable & Intersection-free Simulation of Stiff Materials
Lei Lan, Danny M. Kaufman, Minchen Li +2
Simulating stiff materials in applications where deformations are either not significant or can safely be ignored is a pivotal task across fields. Rigid body modeling has thus long…
A Large-Scale Benchmark for the Incompressible Navier-Stokes Equations
Zizhou Huang, Teseo Schneider, Minchen Li +3
We introduce a collection of benchmark problems in 2D and 3D (geometry description and boundary conditions), including simple cases with known analytic solution, classical experime…
Ships, Splashes, and Waves on a Vast Ocean
Libo Huang, Ziyin Qu, Xun Tan +3
The simulation of large open water surface is challenging using a uniform volumetric discretization of the Navier-Stokes equations. Simulating water splashes near moving objects, w…
BFEMP: Interpenetration-Free MPM-FEM Coupling with Barrier Contact
Xuan Li, Yu Fang, Minchen Li +1
This paper introduces BFEMP, a new approach for monolithically coupling the Material Point Method (MPM) with the Finite Element Method (FEM) through barrier energy-based particle-m…
A barrier method for frictional contact on embedded interfaces
Yidong Zhao, Jinhyun Choo, Yupeng Jiang +3
We present a barrier method for treating frictional contact on interfaces embedded in finite elements. The barrier treatment has several attractive features, including: (i) it does…
High-order Differentiable Autoencoder for Nonlinear Model Reduction
Siyuan Shen, Yang Yin, Tianjia Shao +4
This paper provides a new avenue for exploiting deep neural networks to improve physics-based simulation. Specifically, we integrate the classic Lagrangian mechanics with a deep au…
Codimensional Incremental Potential Contact
Minchen Li, Danny M. Kaufman, Chenfanfu Jiang
We extend the incremental potential contact (IPC) model for contacting elastodynamics to resolve systems composed of codimensional DOFs in arbitrary combination. This enables a uni…
Soft Hybrid Aerial Vehicle via Bistable Mechanism
Xuan Li, Jessica McWilliams, Minchen Li +2
Unmanned aerial vehicles have been demonstrated successfully in a variety of tasks, including surveying and sampling tasks over large areas. These vehicles can take many forms. Qua…
Second-order Neural Network Training Using Complex-step Directional Derivative
Siyuan Shen, Tianjia Shao, Kun Zhou +3
While the superior performance of second-order optimization methods such as Newton's method is well known, they are hardly used in practice for deep learning because neither assemb…
A Hybrid Lagrangian/Eulerian Collocated Advection and Projection Method for Fluid Simulation
Steven W. Gagniere, David A. B. Hyde, Alan Marquez-Razon +5
We present a hybrid particle/grid approach for simulating incompressible fluids on collocated velocity grids. We interchangeably use particle and grid representations of transporte…
Lagrangian-Eulerian Multi-Density Topology Optimization with the Material Point Method
Yue Li, Xuan Li, Minchen Li +3
In this paper, a hybrid Lagrangian-Eulerian topology optimization (LETO) method is proposed to solve the elastic force equilibrium with the Material Point Method (MPM). LETO transf…