Category Archives: Uncategorized

Yura Hwang, Jenny Han Lin, Jerry Hsu, Benjamin Mastripolito, James McCann, Cem Yuksel Lightweight, mesh-level models of knit fabric behavior are useful for both interactive pattern editing and initialization of yarn-level simulations. However, existing mesh-level simulation methods abstract knitting as … Continue reading →

J. Andreas Bærentzen, Jonàs Martínez, Jeppe Revall Frisvad, Sylvain Lefebvre We introduce a divergence-free nD vector noise defined as the n-dimensional cross product of the gradients of n − 1 noise functions. We show that this vector noise function is … Continue reading →

Yi-Lu Chen, Mickaël Ly, Chris Wojtan The realistic simulation of sand, soil, powders, rubble piles, and large collections of rigid bodies is a common and important problem in the fields of computer graphics, computational physics, and engineering. Direct simulation of … Continue reading →

Otman Benchekroun, Eitan Grinspun, Maurizio Chiaramonte, Philip Allen Etter Designing subspaces for Reduced Order Modeling (ROM) is crucial for accelerating finite element simulations in graphics and engineering. Unfortunately, it’s not always clear which subspace is optimal for arbitrary dynamic simulation. … Continue reading →

Jie Chen, Zherong Pan, Bo Ren We propose a Reinforcement Learning (RL) algorithm that combines several novel techniques to achieve more stable and robust control results for coupled solid-fluid systems. Our method utilizes the twin-delayed actor-critic algorithm to efficiently utilize … Continue reading →

Zhiyang Dou, Chen Peng, Xinyu Lu, Xiaohan Ye, Lixing Fang, Yuan Liu, Wenping Wang, Chuang Gan, Lingjie Liu, Taku Komura Humans possess the ability to master a wide range of motor skills, enabling them to quickly and flexibly adapt to … Continue reading →

Jiwei Wang, Wenbin Song, Yicheng Fan, Yang Wang, Xiaopei Liu Unmanned aerial vehicles (UAVs) have demonstrated remarkable efficacy across diverse fields. Nevertheless, developing flight controllers tailored to a specific UAV design, particularly in environments with strong fluid-interactive dynamics, remains challenging. … Continue reading →

Yibo Liu, Zhixin Fang, Sune Darkner, Noam Aigerman, Kenny Erleben, Paul Kry, Teseo Schneider We propose mesh-free fluid simulations that exploit a kinematic neural basis for velocity fields represented by an MLP. We design a set of losses that ensures … Continue reading →

Mengfei Liu, Yue Chang, Zhecheng Wang, Peter Yichen Chen, Eitan Grinspun We introduce a neural field construction that captures gradient discontinuities without baking their location into the network weights. By augmenting input coordinates with a smoothly clamped distance function in … Continue reading →

Jia-Ming Lu, Geng-Chen Cao, Chenfeng Li, Shi-Min Hu This paper proposes a novel simulation approach that combines implicit integration with the Bonded Discrete Element Method (BDEM) to achieve faster, more stable and more accurate fracture simulation. The new method leverages … Continue reading →