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.

翻译：时空流动支配着物理学、生物学和工程学中的多种现象，然而对其多尺度动力学进行建模仍然是一个核心挑战。尽管物理信息机器学习取得了重大进展，但现有方法难以在混沌、湍流和生理学体系中同时维持长期时间演化并解析精细尺度结构。本文提出Uni-Flow，一个统一的自回归-扩散框架，该框架将时间演化与空间细化显式分离，用于建模复杂动力系统。自回归组件学习低分辨率潜在动力学，以保持大尺度结构并确保稳定的长时程推演，而扩散组件则重建高分辨率物理场，通过少量去噪步骤恢复精细尺度特征。我们在多个经典基准测试中验证了Uni-Flow，包括二维Kolmogorov流动、采用量子信息自回归先验的三维湍流通道入流生成，以及基于高保真格子玻尔兹曼血流动力学求解器的患者特异性主动脉缩窄模拟。在心血管场景中，Uni-Flow实现了任务级快于实时的脉动血流动力学推理，能在数秒而非数小时内重建生理相关时间尺度上的高分辨率压力场。通过将高保真血流动力学模拟从离线、受限于高性能计算的过程转变为可部署的代理模型，Uni-Flow为复杂多尺度流动的快于实时建模开辟了道路，对流动物理中的科学机器学习具有广泛意义。