CraftSPH：一个用PyTorch实现的高精度、可组合的可微SPH求解器
CraftSPH: A high-accuracy and composable differentiable SPH solver implemented in PyTorch
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中文总结 AI 辅助
CraftSPH是一个基于PyTorch的高精度可组合可微SPH求解器，通过模块化设计支持多种数值格式与自动微分，适用于正向模拟及物理参数估计等逆问题。
中文摘要 AI 辅助
光滑粒子流体动力学（SPH）适用于一系列问题，特别是涉及流体动力学中大变形的问题。近年来，除了SPH公式的改进外，可微求解器的发展也取得了进展。然而，能够灵活容纳多种数值格式（包括高级和隐式方法）并支持持续扩展和更新的统一框架仍然有限。在本研究中，开发了一个高精度、可组合的可微SPH求解器CraftSPH。在CraftSPH中，主要的计算操作被实现为独立模块，可以根据构建求解器的预期目的进行组合。这种设计使得不同的计算方案可以通过常见组件的组合直观地构建，并允许灵活地纳入和扩展新开发的高精度离散化方法。此外，每个模块都支持自动微分，从而能够应用于物理参数估计以及将SPH求解器与深度学习模型相结合的优化。因此，CraftSPH提供了一个SPH计算框架，在该框架中，高精度离散化、显式和隐式计算以及自动微分可以以统一且可扩展的方式处理。为了展示CraftSPH对广泛问题的适用性，对泊肃叶流动、二维和三维溃坝问题以及上升气泡问题进行了正向分析。此外，通过涉及泰勒-格林涡和顶盖驱动腔流参数估计的逆问题，评估了自动微分的性能。
英文摘要
Smoothed Particle Hydrodynamics (SPH) is well suited to a range of problems, particularly those involving large deformations in fluid dynamics. In recent years, in addition to the advancement of SPH formulations, the development of differentiable solvers has also progressed. However, unified frameworks that flexibly accommodate diverse numerical schemes, including advanced and implicit methods, while supporting continuous extension and updating remain limited. In this study, a high-accuracy and composable differentiable SPH solver, CraftSPH, has been developed. In CraftSPH, major computational operations are implemented as independent modules, which can be combined according to the intended purpose of constructing a solver. This design enables different computational schemes to be intuitively constructed from combinations of common components and allows newly developed high-accuracy discretization methods to be flexibly incorporated and extended. Furthermore, each module supports automatic differentiation, enabling applications to physical-parameter estimation and to optimization that combines SPH solvers with deep learning models. Accordingly, CraftSPH provides an SPH computational framework in which high-accuracy discretization, explicit and implicit computations, and automatic differentiation can be handled in a unified and extensible manner. To demonstrate the applicability of CraftSPH to a wide range of problems, forward analyses are conducted for Poiseuille flow, two- and three-dimensional dam-break problems, and a rising-bubble problem. In addition, the performance of automatic differentiation is evaluated through inverse problems involving parameter estimation for the Taylor-Green vortex and lid-driven cavity flow.
发表机构
- University of Tsukuba（筑波大学）
- Kyushu University（九州大学）
机构由 AI 辅助整理，请以论文原文为准。
