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.
Tensor networks (TNs), originally developed for simulating many-body quantum systems, provide a systematic framework for approximating high-dimensional fields. This is achieved by factorizing the field into interconnected tensors with small bond dimensions, thereby restricting the correlations captured across field bip...
M. Esmaeili, Hirad Alipanah, Robert Pinkston et al.· 1 citation
How spatial structures manifest in multi-time correlations is a fundamental question in turbulence. We develop a non-Hermitian bosonic framework that unifies equal-time anomalous statistics and their temporal propagation within a common operator representation. A continuous Wegner flow reorganizes stochastic mode coupl...
Zhao-Yuan Meng, Long Wang, Guo-Wei He· 0 citations
We use a large database of direct numerical simulations to investigate the transition of the Rayleigh--Taylor instability to turbulence and its evolution toward a late-time self-similar regime. In addition to tracking the growth of the mixing layer through the mean heavy-fluid concentration profile, we analyze one-dime...
Téo Granger, B. Nadiga, B. Gréa et al.· 0 citations
Turbulent polymeric flows show strong deviations from Kolomogorov-like behaviour resulting from more complex dynamics compared to Newtonian turbulence. We now study the nature of mixing in polymeric turbulence via Eulerian passive scalar fields of varying molecular diffusivities, given by the Schmidt number Sc. We sh...
R. K. Singh, M. Rosti· Europhysics letters· 0 citations
It is concluded that aligning data-driven methods with physical constraints—and the training objective with deployment—is the path toward reliable, generalizable, and computationally efficient turbulence models for engineering applications.
Jia-Hao Hu, Jun-Ya Yang· The Physics of Fluids· 0 citations
A salient feature of fully turbulent flows far from onset is the intermittent occurrence of extreme fluctuations at small spatial and temporal scales. These have a qualitative and quantitative effect on the instantaneous curvature of a tracer particle trajectory as an intrinsically multi-scale observable. Here, we prov...
Yasmin Hengster, J. Bosbach, D. Schanz et al.· 0 citations
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