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Compression, simulation, and synthesis of turbulent flows with tensor trains

Stefano Pisoni1,2,*, Raghavendra D. Peddinti1, Siddhartha E. Guzman1, Egor Tiunov1, and Leandro Aolita1

  • *Contact author: stefano.pisoni@tii.ae
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Phys. Rev. Research 8, 023245 – Published 4 June, 2026

DOI: https://doi.org/10.1103/v8c9-9ccm

Abstract

Numerical simulations of turbulent fluids are paramount to real-life applications, from predicting and modeling flows to diagnostic purposes in engineering. However, they are also computationally challenging due to their intrinsically nonlinear dynamics, which require a very high spatial resolution to accurately describe them. A promising idea is to represent flows on a discrete mesh using tensor trains (TTs), featuring a convenient scaling of the number of parameters with the mesh size. However, it is unclear how the compression power of TTs is affected by the complexity of the flows, as measured by the Reynolds number. In fact, no comprehensive analysis of how the TT representation affects the turbulent properties has yet been carried out. We fill this gap by analyzing TTs as an ansatz to compress, simulate, and generate three-dimensional (3D) snapshots with turbulentlike features. Specifically, we first investigate the effect of TT compression on key turbulence signatures, such as the energy spectrum, the probability distribution function of velocity increments, and flatness. Second, we extend the two-dimensional TT solver introduced by Peddinti et al. [Commun. Phys. 7, 135 (2024)] to a 3D cubic domain with periodic boundary conditions. We use it to simulate the incompressible Navier-Stokes dynamics at Reλ=315 for a total of 9–10 Kolmogorov turnover times, showcasing the numerical stability of the TT solver in fully developed turbulent regimes. Third, we develop a TT algorithm to synthesize artificial snapshots that exhibit turbulentlike features, with a logarithmic cost in the mesh size. Our analysis demonstrates the ability of the TT representation to capture the characteristic features of turbulence. This offers a powerful quantum-inspired toolkit for the computational treatment of turbulent flows.

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Phys. Rev. Research 8, 023245– Published 4 June, 2026
  • Received 10 July 2025
  • Accepted 11 May 2026
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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