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
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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.
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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.