The affine particle-in-cell method

By Chenfanfu Jiang, Craig A. Schroeder, Andrew Selle, Joseph M. Teran, Alexey Stomakhin. ACM Transactions on Graphics, 2015

476 citations recorded by the source corpus.

Subfields: Computational Mechanics

View Paper: https://www.semanticscholar.org/paper/6bcf690a1ce95a12b521c0930eea0ebdfeff2ad0

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In Computational Mechanics

Citations in the corpus

PhysGaussian: Physics-Integrated 3D Gaussians for Generative Dynamics (2023, unknown): “The inherent capability of the MPM system allows for topology changes and frictional interactions, making it suitable for simulating various materials, including but not limited to elastic objects, fluids, sand, and snow [13, 17, 39].”
ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics (2018, unknown): “Particles transfer mass mp, momentum (mv) n p , and stress-contributed impulse to their neighbouring grid nodes, using the Affine Particle-in-Cell method (APIC) [28] and moving least squares force discretization [8], weighted by a compact B-spline kernel N : (Gp below is a temporary tensor) mni = ∑”
The material point method for simulating continuum materials (2016, methodology): “10.1 APIC Transfers The Affine Particle-In-Cell (APIC) method [Jiang et al., 2015] is recommended for particlegrid transfers due to its nice numerical properties.”
A multi-scale model for simulating liquid-hair interactions (2017, methodology): “To simulate bulk volumes of liquid not attached to the hair itself, we adopt the affine particle-in-cell (APIC) method [Jiang et al. 2015], which offers reduced velocity dissipation and good preservation of linear and angular momentum.”
Eulerian solid-fluid coupling (2016, methodology): “Recently, the Material Point Method (MPM) [Stomakhin et al. 2014; Jiang et al. 2015] has become popular for simulating a number of mixed-phase phenomena.”
A moving least squares material point method with displacement discontinuity and two-way rigid body coupling (2018, methodology): “We show that the low dissipation A ne PIC[Jiang et al. 2015, 2017b] and Polynomial PIC [Fu et al. 2017] methods can be derived from a Galerkin-style Moving Least Squares (MLS) discretization of the governing equations.”
Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense (2020, methodology): “In particular, as a generalization of the hybrid fluid implicit particle (FLIP) method [403,404] from computational fluid dynamics to computational solid mechanics, the MPM has proven to be a promising discretization choice for simulating many solid and fluid materials since its introduction two dec”