Category Archives: Uncategorized

Mickeal Verschoor, Andrei C. Jalba Simulating (elastically) deformable models that can collide with each other and with the environment remains a challenging task. The resulting contact problems can be elegantly approached using Lagrange multipliers to represent the unknown magnitude of … Continue reading →

Byungsoo Kim, Vinicius C. Azevedo, Nils Thuerey, Theodore Kim, Markus Gross, Barbara Solenthaler This paper presents a novel generative model to synthesize fluid simulations from a set of reduced parameters. A convolutional neural network is trained on a collection of … Continue reading →

Ying Wang, Nicolas J. Weidner, Margaret A. Baxter, Yura Hwang, Danny Kaufman, Shinjiro Sueda It is well known that the dynamics of articulated rigid bodies can be solved in O(n) time using a recursive method, where n is the number … Continue reading →

Ryan Goldade, Yipeng Wang, Mridul Aanjaneya, Christopher Batty While pressure forces are often the bottleneck in (near-)inviscid fluid simulations, viscosity can impose orders of magnitude greater computational costs at lower Reynolds numbers. We propose an implicit octree finite difference discretization … Continue reading →

Lawson Fulton, Vismay Modi, David Duvenaud, David I. W. Levin, Alec Jacobson We propose the first reduced model simulation framework for deformable solid dynamics using autoencoder neural networks.We provide a data-driven approach to generating nonlinear reduced spaces for deformation dynamics. … Continue reading →

Y. Cortial, A. Peytavie, E. Galin, E. Guérin We present a procedural method for authoring synthetic tectonic planets. Instead of relying on computationally demanding physically-based simulations, we capture the fundamental phenomena into a procedural method that faithfully reproduces large-scale planetary … Continue reading →

Igor Santesteban, Miguel A. Otaduy, Dan Casas This paper presents a learning-based clothing animation method for highly efficient virtual try-on simulation. Given a garment, we preprocess a rich database of physically-based dressed character simulations, for multiple body shapes and animations. … Continue reading →

Steffen Wiewel, Moritz Becher, Nils Thuerey We propose a method for the data-driven inference of temporal evolutions of physical functions with deep learning. More specifically, we target fluid flows, i.e. Navier-Stokes problems, and we propose a novel LSTM-based approach to … Continue reading →

A Geometrically Consistent Viscous Fluid Solver with Two-Way Fluid-Solid Coupling Deep Fluids: A Generative Network for Parameterized Fluid Simulations Latent Space Physics: Towards Learning the Temporal Evolution of Fluid Flow Learning-Based Animation of Clothing for Virtual Try-On Procedural Tectonic Plates … Continue reading →

Ziyin Qu*, Xinxin Zhang* (joint 1st authors), Ming Gao, Chenfanfu Jiang, Baoquan Chen In this paper, we introduce BiMocq2, an unconditionally stable, pure Eulerian-based advection scheme to efficiently preserve the advection accuracy of all physical quantities for long-term fluid simulations. … Continue reading →