JAX-SPH: A Differentiable Smoothed Particle Hydrodynamics Framework
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929412446879744 |
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| author | Toshev, Artur P. Ramachandran, Harish Erbesdobler, Jonas A. Galletti, Gianluca Brandstetter, Johannes Adams, Nikolaus A. |
| author_facet | Toshev, Artur P. Ramachandran, Harish Erbesdobler, Jonas A. Galletti, Gianluca Brandstetter, Johannes Adams, Nikolaus A. |
| contents | Particle-based fluid simulations have emerged as a powerful tool for solving the Navier-Stokes equations, especially in cases that include intricate physics and free surfaces. The recent addition of machine learning methods to the toolbox for solving such problems is pushing the boundary of the quality vs. speed tradeoff of such numerical simulations. In this work, we lead the way to Lagrangian fluid simulators compatible with deep learning frameworks, and propose JAX-SPH - a Smoothed Particle Hydrodynamics (SPH) framework implemented in JAX. JAX-SPH builds on the code for dataset generation from the LagrangeBench project (Toshev et al., 2023) and extends this code in multiple ways: (a) integration of further key SPH algorithms, (b) restructuring the code toward a Python package, (c) verification of the gradients through the solver, and (d) demonstration of the utility of the gradients for solving inverse problems as well as a Solver-in-the-Loop application. Our code is available at https://github.com/tumaer/jax-sph. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_04750 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | JAX-SPH: A Differentiable Smoothed Particle Hydrodynamics Framework Toshev, Artur P. Ramachandran, Harish Erbesdobler, Jonas A. Galletti, Gianluca Brandstetter, Johannes Adams, Nikolaus A. Fluid Dynamics Machine Learning Particle-based fluid simulations have emerged as a powerful tool for solving the Navier-Stokes equations, especially in cases that include intricate physics and free surfaces. The recent addition of machine learning methods to the toolbox for solving such problems is pushing the boundary of the quality vs. speed tradeoff of such numerical simulations. In this work, we lead the way to Lagrangian fluid simulators compatible with deep learning frameworks, and propose JAX-SPH - a Smoothed Particle Hydrodynamics (SPH) framework implemented in JAX. JAX-SPH builds on the code for dataset generation from the LagrangeBench project (Toshev et al., 2023) and extends this code in multiple ways: (a) integration of further key SPH algorithms, (b) restructuring the code toward a Python package, (c) verification of the gradients through the solver, and (d) demonstration of the utility of the gradients for solving inverse problems as well as a Solver-in-the-Loop application. Our code is available at https://github.com/tumaer/jax-sph. |
| title | JAX-SPH: A Differentiable Smoothed Particle Hydrodynamics Framework |
| topic | Fluid Dynamics Machine Learning |
| url | https://arxiv.org/abs/2403.04750 |