# yuanming-hu/taichi_mpm

High-performance moving least squares material point method (MLS-MPM) solver. (ACM Transactions on Graphics, SIGGRAPH 2018)

Repository: https://github.com/yuanming-hu/taichi_mpm
Canonical: https://ross.abutalabs.com/products/taichi_mpm
Language: C++
License: MIT
License Family: permissive
Last push: 2022-02-23T05:43:10+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 2944, "days_push": 1652, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2578, forks 322 (observed 2026-08-28T04:07:02.311813+00:00)

## What it is
A high-performance C++ implementation of the Moving Least Squares Material Point Method (MLS-MPM) with cutting and two-way rigid body coupling, published at SIGGRAPH 2018. It includes an educational 88-line version and serves as reference code for the accompanying research paper.

## Use cases
- simulate sand, snow, and other granular materials with MPM
- learn how the material point method works from a compact code example
- reproduce MLS-MPM research results from the SIGGRAPH 2018 paper
- simulate fluid-structure interaction with rigid body coupling
- build a physics-based material simulation for graphics research

## When to choose
- you need a fast, reference-quality MPM solver for research or graphics
- you want readable educational code explaining MLS-MPM
- you need cutting and rigid-body coupling in a particle simulation

## When to avoid
- you need a maintained production library with active support
- you want a general-purpose physics engine for games
- you need Python-first APIs (consider Taichi instead)

## Facets
- artifact type: library
- maturity: maintenance
- function: simulation, graphics
- domain: simulation, graphics, computer-vision
- platform: cpp, windows, cross-platform
- tags: mpm, material-point-method, physics-simulation, siggraph, research-code, cpic, numerical-methods, physics, linux, macos

## Member repositories
- yuanming-hu/taichi_mpm (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:02.311813+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:22:40.985364+00:00, confidence not recorded.
  - readme: https://github.com/yuanming-hu/taichi_mpm (fetched 2026-08-28T04:07:02.311813+00:00, sha ac3c03b8241c)
- Data as of 2026-08-30T08:39:29.467469+00:00.