1 citations · 1 across the 12 of their papers we have counts for
15 papers
Scaling Online Optimal Transport Minibatches for Flow Matching
Peter Chen, Sergey Levine
A central challenge in scaling flow models is maintaining stability in training and integration. In flow matching, this stability depends critically on how source samples are paire…
BoxTwin: Learning Elastoplastic Articulated Object Dynamics from Videos
Heng Zhang, Gehan Zheng, Kaifeng Zhang +6
Digital twins enable robots to anticipate and adapt to physical interactions, but existing models struggle with elastoplastic articulated objects (EAOs) that exhibit nonlinear elas…
MeGAS: Thermomechanical Dynamic Gaussian Splatting for Thermophysical Scene Editing
Zesong Yang, Yuanhang Lei, Liyuan Cui +6
Recent advances integrate physically grounded Newtonian dynamics with neural rendering frameworks, narrowing the gap between photorealistic scene reconstruction and physics-based a…
PINNsur: Physics-Informed Neural Networks for PDEs on Curved Surfaces
Pranav Jain, Navami Kairanda, Peter Yichen Chen +1
Partial differential equations (PDEs) on surfaces are fundamental to scientific computing and geometry processing. A popular approach to solving PDEs on surfaces is the finite elem…
EUPHORIA: Efficient Universal Planning via Hybrid Optimization for Robust Industrial Robotic Assembly
Shih-Yu Lai, Chia-Ching Yen, Yang-Ting Shen +3
Robotic assembly in architectural construction faces a persistent bottleneck: existing planners are either highly specialized, requiring prohibitive retraining for every new geomet…
DiffPhD: A Unified Differentiable Solver for Projective Heterogeneous Materials in Elastodynamics with Contact-Rich GPU-Acceleration
Shih-Yu Lai, Sung-Han Tien, Jui-I Huang +9
Differentiable simulation of soft bodies is a foundation for system identification, trajectory optimization, and Real2Sim transfer. Yet, existing methods such as the differentiable…
Closing Trajectories: Equation-Free Cyclic Animation via Koopman Surrogates
Shixun Huang, Siyuan Chen, Yue Chang +2
Cyclic animation is widely used in computer graphics and interactive content.It supports seamless playback in games, VR, and interactive simulation,where short clips must repeat sm…
PhysSkin: Real-Time and Generalizable Physics-Based Animation via Self-Supervised Neural Skinning
Yuanhang Lei, Tao Cheng, Xingxuan Li +6
Achieving real-time physics-based animation that generalizes across diverse 3D shapes and discretizations remains a fundamental challenge. We introduce PhysSkin, a physics-informed…
Low-Rank Koopman Deformables with Log-Linear Time Integration
Yue Chang, Peter Yichen Chen, Eitan Grinspun +1
We present a low-rank Koopman operator formulation for accelerating deformable subspace simulation. Using a Dynamic Mode Decomposition (DMD) parameterization of the Koopman operato…
Precise Gradient Discontinuities in Neural Fields for Subspace Physics
Mengfei Liu, Yue Chang, Zhecheng Wang +2
Discontinuities in spatial derivatives appear in a wide range of physical systems, from creased thin sheets to materials with sharp stiffness transitions. Accurately modeling these…
3D Cell Oversegmentation Correction via Geo-Wasserstein Divergence
Peter Chen, Bryan Chang, Olivia A Creasey +3
3D cell segmentation methods are often hindered by \emph{oversegmentation}, where a single cell is incorrectly split into multiple fragments. This degrades the final segmentation q…
Displacement-Sparse Neural Optimal Transport
Peter Chen, Yue Xie, Qingpeng Zhang
Optimal transport (OT) aims to find a map that transports mass from one probability measure to another while minimizing a cost function. Recently, neural OT solvers have gained…
Lifting the Winding Number: Precise Discontinuities in Neural Fields for Physics Simulation
Yue Chang, Mengfei Liu, Zhecheng Wang +2
Cutting thin-walled deformable structures is common in daily life, but poses significant challenges for simulation due to the introduced spatial discontinuities. Traditional method…
Shape Space Spectra
Yue Chang, Otman Benchekroun, Maurizio M. Chiaramonte +2
Eigenanalysis of differential operators, such as the Laplace operator or elastic energy Hessian, is typically restricted to a single shape and its discretization, limiting reduced…
Learning Lagrangian Interaction Dynamics with Sampling-Based Model Order Reduction
Hrishikesh Viswanath, Yue Chang, Aleksey Panas +3
Simulating physical systems governed by Lagrangian dynamics often entails solving partial differential equations (PDEs) over high-resolution spatial domains, leading to significant…