GeoWarp: An automatically differentiable and GPU-accelerated implicit MPM framework for geomechanics based on NVIDIA Warp
Yidong Zhao, Xuan Li, Chenfanfu Jiang, Jinhyun Choo

TL;DR
GeoWarp is a GPU-accelerated, differentiable implicit MPM framework for geomechanics that simplifies Jacobian computation using automatic differentiation and sparse algorithms, enabling scalable large-deformation simulations.
Contribution
It introduces GeoWarp, a novel implicit MPM framework leveraging automatic differentiation and GPU parallelism for efficient geomechanics simulations.
Findings
Successfully verified in elastoplasticity and poromechanics examples.
Achieves scalable and robust implicit MPM simulations.
Eliminates manual Jacobian derivation through automatic differentiation.
Abstract
The material point method (MPM), a hybrid Lagrangian-Eulerian particle method, is increasingly used to simulate large-deformation and history-dependent behavior of geomaterials. While explicit time integration dominates current MPM implementations due to its algorithmic simplicity, such schemes are unsuitable for quasi-static and long-term processes typical in geomechanics. Implicit MPM formulations are free of these limitations but remain less adopted, largely due to the difficulty of computing the Jacobian matrix required for Newton-type solvers, especially when consistent tangent operators should be derived for complex constitutive models. In this paper, we introduce GeoWarp -- an implicit MPM framework for geomechanics built on NVIDIA Warp -- that exploits GPU parallelism and reverse-mode automatic differentiation to compute Jacobians without manual derivation. To enhance…
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Taxonomy
TopicsFluid Dynamics Simulations and Interactions · Tunneling and Rock Mechanics · Metallurgy and Material Forming
