A moving least squares material point method with displacement discontinuity and two-way rigid body coupling

By Yuanming Hu, Yu Fang, Z. Ge, Ziyin Qu, Yixin Zhu, Andre Pradhana, Chenfanfu Jiang. 2018

401 citations recorded by the source corpus.

In this paper, we introduce the Moving Least Squares Material Point Method (MLS-MPM). MLS-MPM naturally leads to the formulation of Affine Particle-In-Cell (APIC) [Jiang et al. 2015] and Polynomial Particle-In-Cell [Fu et al. 2017] in a way that is consistent with a Galerkin-style weak form discretization of the governing equations. Additionally, it enables a new stress divergence discretization that effortlessly allows all MPM simulations to run two times faster than before. We also develop a Compatible Particle-In-Cell (CPIC) algorithm on top of MLS-MPM. Utilizing a colored distance field representation and a novel compatibility condition for particles and grid nodes, our framework enables the simulation of various new phenomena that are not previously supported by MPM, including material cutting, dynamic open boundaries, and two-way coupling with rigid bodies. MLS-MPM with CPIC is easy to implement and friendly to performance optimization.

Subfields: Atomic and Molecular Physics, and Optics, Computational Mechanics, Mechanics of Materials

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Citations in the corpus

Learning to Simulate Complex Physics with Graph Networks (2020, methodology): “For most of our domains, we use the Taichi-MPM engine (Hu et al., 2018) to simulate a variety of challenging 2D and 3D scenarios.”
PhysGaussian: Physics-Integrated 3D Gaussians for Generative Dynamics (2023, background): “The Material Point Method (MPM) is a widely used simulation framework for a broad range of multi-physics phenomena [10].”
DiffTaichi: Differentiable Programming for Physical Simulation (2019, methodology): “We follow ChainQueen’s implementation (Hu et al., 2019b) and use the moving least squares material point method (Hu et al., 2018) to simulate the system.”
ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics (2018, neutral): “ChainQueen’s support for differentiation (i.e. gradient computation) is what makes it different from existing soft object simulators (e.g., the recent work on MLS-MPM [8]).”
ManiSkill2: A Unified Benchmark for Generalizable Manipulation Skills (2023, methodology): “Rigid-soft Coupling : Our MPM solver is MLS-MPM (Hu et al., 2018) similar to PlasticineLab (Huang et al., 2021), but with a different contact modeling approach, which enables 2-way coupling with external rigid-body simulators.”
Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense (2020, background): “ing, existing work includes snow [407, 408], foam [409, 410, 411], sand [412, 413], rigid body [414], fracture [415, 416], cloth [417], hair [418], water [419], and solid-fluid mixtures [420, 421, 422].”
A Survey on 3D Gaussian Splatting (2024, neutral): “Existing methods differ mainly in how they couple Gaussians with explicit simulators.”