Monthly Archives: October 2023

Han Yan, Bo Ren Multiple fluid simulation has raised wide research interest in recent years. Despite the impressive successes of current works, simulation of scenes containing mixing or unmixing of high-density-ratio phases using particle-based discretizations still remains a challenging task. … Continue reading →

Xingqiao Li*, Xingyu Ni*, Bo Zhu, Bin Wang, and Baoquan Chen (* = joint first authors) This paper presents a novel level-set method that combines gradient augmentation and reference mapping to enable high-fidelity interface tracking and surface tension flow simulation, … Continue reading →

Zhehao Li, Qingyu Xu, Xiaohan Ye, Bo Ren, Ligang Liu Differentiable physics simulation has shown its efficacy in inverse designproblems. Given the pervasiveness of the diverse interactions between fluids and solids in life, a differentiable simulator for the inverse design … Continue reading →

Huanyu Chen, Danyong Zhao, Jernej Barbič Capturing material properties of real-world elastic solids is both challenging and highly relevant to many applications in computer graphics, robotics and related fields. We give a non-intrusive, in-situ and inexpensive approach to measure the … Continue reading →

Qiqin Le, Yitong Deng, Jiamu Bu, Bo Zhu, Tao Du We present a computational framework for simulating deformable surfaces with second-order triangular finite elements. Our method develops numerical schemes for discretizing stretching, shearing, and bending energies of deformable surfaces in … Continue reading →

Yitong Deng, Hong-Xing Yu, Diyang Zhang, Jiajun Wu, Bo Zhu We introduce Neural Flow Maps, a novel simulation method bridging the emerging paradigm of implicit neural representations with fluid simulation based on the theory of flow maps, to achieve state-of-the-art … Continue reading →

Jiahao Wen, Jernej Barbič Kirchhoff-Love shells are commonly used in many branches of engineering, including in computer graphics, but have so far been simulated only under limited nonlinear material options. We derive the Kirchhoff-Love thin-shell mechanical energy for an arbitrary … Continue reading →

Zeshun Zong, Xuan Li, Minchen Li, Maurizio M. Chiaramonte, Wojciech Matusik, Eitan Grinspun, Kevin Carlberg, Chenfanfu Jiang, Peter Yichen Chen We propose a hybrid neural network and physics framework for reduced-order modeling of elastoplasticity and fracture. State-of-the-art scientific computing models … Continue reading →

Ziyin Qu, Minchen Li, Yin Yang, Chenfanfu Jiang, Fernando de Goes We present a novel hybrid Lagrangian/Eulerian method for simulating inelastic flows that generates high-quality particle distributions with adaptive volumes. At its core, our approach integrates an updated Lagrangian time … Continue reading →

F. Maggioli, J. Klein, T. Hädrich, E. Rodolà, W. Pałubicki, S. Pirk, D. L. Michels. Plants are among the most complex objects to be modeled in computer graphics. While a large body of work is concerned with structural modeling and … Continue reading →