Tensor networks enable tunable, objective compression of 1D fluid data with lossless reconstruction at high bond dimension and efficient in-compressed-space operations like periodic convolution.
Tensornetworkreducedordermodelsforwall-boundedflows
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Tensor train methods compress and accelerate simple GFD flows but struggle to represent complex realistic states in shallow water equation tests.
The paper investigates the effects of time integrator selection, numerical dissipation, and problem representation on the efficiency and stability of quantized tensor train simulations for advection-dominated test problems.
citing papers explorer
-
Tensor network compression using fluid dynamics as a testbed: Analytical foundations in one dimension
Tensor networks enable tunable, objective compression of 1D fluid data with lossless reconstruction at high bond dimension and efficient in-compressed-space operations like periodic convolution.
-
Viability of Tensor Train Methods for Geophysical Fluid Dynamics
Tensor train methods compress and accelerate simple GFD flows but struggle to represent complex realistic states in shallow water equation tests.
-
A practical investigation on time integration in the quantized tensor train format
The paper investigates the effects of time integrator selection, numerical dissipation, and problem representation on the efficiency and stability of quantized tensor train simulations for advection-dominated test problems.