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

2026-02-17

AI Summary

Existing methods struggle to simultaneously capture the long-term temporal evolution and fine-scale spatial structures of complex multiscale flows, particularly in chaotic, turbulent, and physiological flow regimes. This work proposes Uni-Flow, a novel framework that unifies autoregressive and diffusion mechanisms for the first time: an autoregressive module learns low-dimensional latent variables to ensure stable long-term dynamics, while a diffusion module efficiently reconstructs high-resolution physical fields. By integrating physics-informed machine learning with high-fidelity lattice Boltzmann data, Uni-Flow achieves high-accuracy, sub-second inference in simulations of Kolmogorov flow, three-dimensional turbulence, and coarctation of the aorta—accelerating traditionally hour-long high-fidelity hemodynamic simulations to real-time performance.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Diffusion Models for Vision
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

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.

Problem

• multiscale flows
• long-term temporal evolution
• fine-scale structure
• complex dynamical systems
• spatiotemporal modeling

Innovation

• unified autoregressive-diffusion
• multiscale flow modeling
• temporal-spatial decoupling
• fast surrogate modeling
• high-fidelity hemodynamics