基于张量列压缩子空间中的多尺度被动标量湍流
Multiscale passive scalar turbulence in a compressed subspace via tensor trains
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中文总结 AI 辅助
针对降阶建模中捕捉湍流多尺度统计的挑战，提出混合张量列方法以改进被动标量间歇性波动表征，为压缩张量形式下演化其线性动力学提供了途径。
中文摘要 AI 辅助
以压缩形式捕捉湍流的多尺度统计特性仍是降阶建模的核心挑战。本文针对高度间歇性的被动标量，提出一种混合张量列（Tensor Train, TT）方法。该混合TT在结构函数上与伽辽金、小波及标准TT分解匹配，同时改进了间歇性非高斯波动的表征。这些结果为直接在压缩张量形式中演化被动标量的线性动力学开辟了途径，在流体输运的量子算法领域具有潜在应用。
英文摘要
Capturing the multiscale statistics of turbulence in compressed form remains a central challenge for reduced-order modeling. We introduce a hybrid Tensor Train (TT) approach for a highly intermittent passive scalar. The hybrid TT matches Galerkin, wavelet, and standard TT decompositions for the structure functions while improving the representation of intermittent, non-Gaussian fluctuations. These results open a route toward evolving the linear dynamics of passive scalars directly in compressed tensor form, with potential applications to quantum algorithms for fluid transport.
