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arXiv · 2608.00194

Multiscale passive scalar turbulence in a compressed subspace via tensor trains

Stefano Pisoni·Egor Tiunov·Chiara Calascibetta
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Abstract

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.

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Stefano Pisoni, Egor Tiunov, Chiara Calascibetta. 2026-07-31. Multiscale passive scalar turbulence in a compressed subspace via tensor trains. https://arxiv.org/abs/2608.00194

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