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arXiv 2301.01718math.NAcs.NAphysics.comp-ph
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基于混合快照的对流占优问题的自适应免训练降阶模型

An adaptive, training-free reduced-order model for convection-dominated problems based on hybrid snapshots

Victor Zucatti, Matthew J. Zahr

AI总结:

针对对流占优问题,提出一种基于混合快照的自适应免训练降阶模型方法,通过动态采样结合高维模型与降阶模型计算并消除伪振荡,在无粘可压缩流问题中实现高达一个数量级的加速。

摘要:

绝大多数降阶模型(ROM)首先从高维模型(HDM)训练数据中获取问题的低维表示,随后将其用于获得一个降低复杂度的系统。遗憾的是,对流占优问题通常具有缓慢衰减的 Kolmogorov n-宽度,这使得仅从训练数据构建准确的 ROM 非常具有挑战性。通过用 HDM 解进行富集可以提升 ROM 的准确度;然而,由于复杂问题的 HDM 计算成本巨大,只能 parsimoniously(精打细算地)使用它们以获取相关的计算节省。在本研究中,我们利用这些问题通常表现出的局部空间和时间相干性,推导出一种准确且高性价比的方法,该方法无需单独的训练阶段,而是反复结合 HDM 和 ROM 计算。我们的方法在给定时间步获取解的方式,要么是完整求解 HDM,要么是结合部分 HDM 和 ROM 求解。动态采样过程识别出需要 HDM 解以保证全局准确性的区域,而流场的其余部分则使用 ROM 重建。此外,结合 HDM 和 ROM 求解的解采用空间滤波,以消除可能产生的潜在伪振荡。我们在无粘可压缩流问题上测试了所提出的方法,并展示了高达一个数量级的加速。

英文摘要:

The vast majority of reduced-order models (ROMs) first obtain a low dimensional representation of the problem from high-dimensional model (HDM) training data which is afterwards used to obtain a system of reduced complexity. Unfortunately, convection-dominated problems generally have a slowly decaying Kolmogorov n-width, which makes obtaining an accurate ROM built solely from training data very challenging. The accuracy of a ROM can be improved through enrichment with HDM solutions; however, due to the large computational expense of HDM evaluations for complex problems, they can only be used parsimoniously to obtain relevant computational savings. In this work, we exploit the local spatial and temporal coherence often exhibited by these problems to derive an accurate, cost-efficient approach that repeatedly combines HDM and ROM evaluations without a separate training phase. Our approach obtains solutions at a given time step by either fully solving the HDM or by combining partial HDM and ROM solves. A dynamic sampling procedure identifies regions that require the HDM solution for global accuracy and the reminder of the flow is reconstructed using the ROM. Moreover, solutions combining both HDM and ROM solves use spatial filtering to eliminate potential spurious oscillations that may develop. We test the proposed method on inviscid compressible flow problems and demonstrate speedups up to an order of magnitude.

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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