14 papers
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…
Factorized Neural Implicit DMD for Parametric Dynamics
Siyuan Chen, Zhecheng Wang, Yixin Chen +4
A data-driven, model-free approach to modeling the temporal evolution of physical systems mitigates the need for explicit knowledge of the governing equations. Even when physical p…
The Configurational Element Method for Nonconvex Granular Media
Zhecheng Wang, Breannan Smith, Abhishek Madan +1
Granular media surround us, comprising everything from the ground we walk on to the foods we eat. Owing to their ubiquity our ability to understand and predict the mechanical evolu…
Variational Green's Functions for Volumetric PDEs
Joao Teixeira, Eitan Grinspun, Otman Benchekroun
Green's functions characterize the fundamental solutions of partial differential equations; they are essential for tasks ranging from shape analysis to physical simulation, yet the…
Topology- and Geometry-Exact Coupling for Incompressible Fluids and Thin Deformables
Jonathan Panuelos, Eitan Grinspun, David Levin
We introduce a topology-preserving discretization for coupling incompressible fluids with thin deformable structures, achieving guaranteed leakproofness through preservation of flu…
Odd-DC: Generalizable Neural Model Reduction via Odd Difference-of-Convex Structure
Shixun Huang, Eitan Grinspun, Yue Chang
Model reduction is essential for real-time simulation of deformable objects. Linear techniques such as PCA provide structured and predictable behavior, but their limited expressive…