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arXiv 2308.12972physics.flu-dynphysics.chem-phphysics.comp-phquant-ph
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面向计算流体力学的完整量子启发框架

Complete quantum-inspired framework for computational fluid dynamics

Raghavendra D. Peddinti, Stefano Pisoni, Alessandro Marini, Philippe Lott, Henrique Argentieri, Egor Tiunov, Leandro Aolita

AI总结:

本文提出基于矩阵乘积态的量子启发全栈框架,以多重对数级复杂度求解任意几何边界下不可压缩流体问题,并直接从压缩编码恢复解,为高效流体求解器奠定基础。

摘要:

计算流体力学既是一个蓬勃发展的研究领域,也是先进工业应用的关键工具。其核心挑战在于模拟复杂几何结构中的湍流,由于离散化网格所需的大向量维度,这是一项计算密集型的任务。我们提出了一种全栈方法,用于求解不可压缩流体问题,其内存和运行时间随网格尺寸呈多重对数级扩展。我们的框架基于矩阵乘积态,这是一种强大的量子态压缩表示。该框架是完整的,因为它能够求解任意几何形状浸没物体周围的流动,并处理非平凡的边界条件;同时它是自洽的,因为它可以直接从压缩编码中恢复解,而无需经过昂贵的稠密向量表示。这一机制为新一代可能从根本上更高效的真实流体问题求解器奠定了基础。

英文摘要:

Computational fluid dynamics is both a thriving research field and a key tool for advanced industry applications. The central challenge is to simulate turbulent flows in complex geometries, a compute-power intensive task due to the large vector dimensions required by discretized meshes. We present a full-stack method to solve for incompressible fluids with memory and runtime scaling poly-logarithmically in the mesh size. Our framework is based on matrix-product states, a powerful compressed representation of quantum states. It is complete in that it solves for flows around immersed objects of arbitrary geometries, with non-trivial boundary conditions, and self-consistent in that it can retrieve the solution directly from the compressed encoding, i.e. without ever passing through the expensive dense-vector representation. This machinery lays the foundations for a new generation of potentially radically more efficient solvers of real-life fluid problems.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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