Uni-Flow: a unified autoregressive-diffusion model for complex multiscale flows

TL;DR

Uni-Flow is a novel unified model combining autoregressive and diffusion methods to accurately and efficiently simulate complex multiscale flows across physics, biology, and engineering, enabling faster-than-real-time predictions.

Contribution

It introduces a new framework that separates temporal dynamics from spatial detail, improving stability and resolution in modeling chaotic and physiological flow regimes.

Findings

Validated on canonical benchmarks including turbulent flows and cardiovascular simulations.

Achieved task-level faster-than-real-time inference of pulsatile hemodynamics.

Transformed high-fidelity simulations into fast, deployable surrogates.

Abstract

Spatiotemporal flows govern diverse phenomena across physics, biology, and engineering, yet modelling their multiscale dynamics remains a central challenge. Despite major advances in physics-informed machine learning, existing approaches struggle to simultaneously maintain long-term temporal evolution and resolve fine-scale structure across chaotic, turbulent, and physiological regimes. Here, we introduce Uni-Flow, a unified autoregressive-diffusion framework that explicitly separates temporal evolution from spatial refinement for modelling complex dynamical systems. The autoregressive component learns low-resolution latent dynamics that preserve large-scale structure and ensure stable long-horizon rollouts, while the diffusion component reconstructs high-resolution physical fields, recovering fine-scale features in a small number of denoising steps. We validate Uni-Flow across canonical benchmarks, including two-dimensional Kolmogorov flow, three-dimensional turbulent channel inflow generation with a quantum-informed autoregressive prior, and patient-specific simulations of aortic coarctation derived from high-fidelity lattice Boltzmann hemodynamic solvers. In the cardiovascular setting, Uni-Flow enables task-level faster than real-time inference of pulsatile hemodynamics, reconstructing high-resolution pressure fields over physiologically relevant time horizons in seconds rather than hours. By transforming high-fidelity hemodynamic simulation from an offline, HPC-bound process into a deployable surrogate, Uni-Flow establishes a pathway to faster-than-real-time modelling of complex multiscale flows, with broad implications for scientific machine learning in flow physics.