Scalable and modular material point method for large-scale simulations
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Abstract (arXiv)
In this paper, we describe a new scalable and modular material point method (MPM) code developed for solving large-scale problems in continuum mechanics. The MPM is a hybrid Eulerian-Lagrangian approach, which uses both moving material points and computational nodes on a background mesh. The MPM has been successfully applied to solve large-deformation problems such as landslides, failure of slopes, concrete flows, etc. Solving these large-deformation problems result in the material points actively moving through the mesh. Developing an efficient parallelisation scheme for the MPM code requires dynamic load-balancing techniques for both the material points and the background mesh. This paper describes the data structures and algorithms employed to improve the performance and portability of the MPM code. An object-oriented programming paradigm is adopted to modularise the MPM code. The Unified Modelling Language (UML) diagram of the MPM code structure is shown in Figure 1.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Sep 2019.
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Citing papers per year: 2022: 1; 2023: 3; 2024: 1; 2025: 4.
9 papers.
- 2023Graph Neural Network-based surrogate model for granular flowscited by 16
- 2023Accelerating Particle and Fluid Simulations with Differentiable Graph Networks for Solving Forward and Inverse Problemscited by 6
- 2024Inverse analysis of granular flows using differentiable graph neural network simulatorcited by 5
- 2025JAX-MPM: A Learning-Augmented Differentiable Meshfree Framework for GPU-Accelerated Lagrangian Simulation and Geophysical Inverse Modelingcited by 5
- 2025Parameter-Efficient Conditioning for Material Generalization in Graph-Based Simulatorscited by 2
- 2022Minority Report: A Graph Network Oracle for In Situ Visualizationcited by 1
- 2023Three-dimensional granular flow simulation using graph neural network-based learned simulatorcited by 1
- 2025MLPs and KANs for data-driven learning in physical problems: A performance comparisoncited by 1
- 2025XDen-1K: A Density Field Dataset of Real-World Objectscited by 0
- 2025XDen-1K: A Density Field Dataset of Real-World Objects11 Dec 2025
- 2025Parameter-Efficient Conditioning for Material Generalization in Graph-Based Simulators7 Nov 2025
- 2025JAX-MPM: A Learning-Augmented Differentiable Meshfree Framework for GPU-Accelerated Lagrangian Simulation and Geophysical Inverse Modeling6 Jul 2025
- 2025MLPs and KANs for data-driven learning in physical problems: A performance comparison15 Apr 2025
- 2024Inverse analysis of granular flows using differentiable graph neural network simulator17 Jan 2024
- 2023Three-dimensional granular flow simulation using graph neural network-based learned simulator13 Nov 2023
- 2023Accelerating Particle and Fluid Simulations with Differentiable Graph Networks for Solving Forward and Inverse Problems23 Sep 2023
- 2023Graph Neural Network-based surrogate model for granular flows9 May 2023
- 2022Minority Report: A Graph Network Oracle for In Situ Visualization25 Jun 2022