Tensor Network Lattice Boltzmann Method for Data-Compressed Fluid Simulations

TL;DR

This paper introduces a tensor network-based lattice Boltzmann method that efficiently simulates complex fluid flows in intricate geometries by compressing data without grid refinement, enabling scalable high-fidelity simulations.

Contribution

It develops a generalized MPS formulation for lattice Boltzmann methods, allowing data compression in complex geometries and flow physics without explicit grid refinement.

Findings

Achieves over 100x data compression while maintaining high accuracy.

Successfully simulates 3D flows in complex geometries.

Demonstrates scalability on GPU hardware.

Abstract

Resolving unsteady transport phenomena in geometrically complex domains is traditionally constrained by polynomial scaling of computational cost with spatial resolution. While methods based on tensor-network data representations or matrix-product states (MPS) data encodings have emerged as a technique to systematically reduce degrees of freedom, existing formulations do not extend to complex geometries and complex flow physics. Both capabilities are offered by lattice Boltzmann methods, for which we develop a generalized MPS formulation. This development marks a paradigm shift from classical methods that rely on explicit grid refinement for data reduction. Instead, our approach exploits non-local correlations in the MPS representation to systemically compress the global fluid state directly without modifying the underlying grid. We benchmark the proposed solver against classical LBM using three-dimensional flows through structured media and vascular geometries. The results confirm that the MPS formulation reproduces the reference solution with high fidelity while achieving compression ratios exceeding two orders of magnitude, positioning tensor networks or MPS encodings as a scalable paradigm for continuum mechanics on high-performance GPU hardware.