LevelSetPy: A GPU-Accelerated Package for Hyperbolic Hamilton–Jacobi Partial Differential Equations

This article introduces a software package release for geometrically reasoning about the safety desiderata of (complex) dynamical systems via level set methods. In emphasis, safety is analyzed with the Hamilton–Jacobi equations. In scope, we provide implementations of numerical algorithms for the resolution of Hamilton–Jacobi–Isaacs equations: the spatial derivatives of the associated value function via upwinding, the Hamiltonian via Lax–Friedrichs schemes, and the integration of the Hamilton–Jacobi equation altogether via total variation diminishing Runge–Kutta schemes. Since computational speed and interoperability with other modern scientific computing libraries (typically written in the Python language) are of essence, we capitalize on modern computational frameworks such as CUPY and NUMPY and move heavy computations to GPU devices to aid parallelization and improve bring-up time in safety analysis. We hope that this package can aid users to quickly iterate on ideas and evaluate all possible safety desiderata of a system via geometrical simulation in modern engineering problems.

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Publication Details

Journal
ACM Transactions on Mathematical Software
Published
2025-04-17
DOI
https://doi.org/10.1145/3730407
Citations
1
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
1.32
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article

LevelSetPy: A GPU-Accelerated Package for Hyperbolic Hamilton–Jacobi Partial Differential Equations

Lekan Molu
1 citations
ACM Transactions on Mathematical Software
Model Reduction and Neural Networks
1.32
article

LevelSetPy: A GPU-Accelerated Package for Hyperbolic Hamilton–Jacobi Partial Differential Equations

Lekan Molu
article en
1 citations

Abstract

This article introduces a software package release for geometrically reasoning about the safety desiderata of (complex) dynamical systems via level set methods. In emphasis, safety is analyzed with the Hamilton–Jacobi equations. In scope, we provide implementations of numerical algorithms for the resolution of Hamilton–Jacobi–Isaacs equations: the spatial derivatives of the associated value function via upwinding, the Hamiltonian via Lax–Friedrichs schemes, and the integration of the Hamilton–Jacobi equation altogether via total variation diminishing Runge–Kutta schemes. Since computational speed and interoperability with other modern scientific computing libraries (typically written in the Python language) are of essence, we capitalize on modern computational frameworks such as CUPY and NUMPY and move heavy computations to GPU devices to aid parallelization and improve bring-up time in safety analysis. We hope that this package can aid users to quickly iterate on ideas and evaluate all possible safety desiderata of a system via geometrical simulation in modern engineering problems.

ACM Transactions on Mathematical SoftwareVol. 51(2)
Microsoft (United States) (US), Microsoft Research (United Kingdom) (GB)
Openalex Percentile: Top 20%
Model Reduction and Neural Networks
1.32
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