用神经网络加速流体模拟，通过残差监控实现自动切换，稳定且提速近3倍。
XRePIT: A deep learning-computational fluid dynamics hybrid framework implemented in OpenFOAM for fast, robust, and scalable unsteady simulations
- 基于残差阈值自动在神经模型与物理求解器间切换
- 3D模拟中实现2.91倍加速，误差保持在千分之一内
- 开源可扩展，适合需要快速高精度流体仿真的研究者
自回归神经代理模型虽能加速流体动力学计算，但长期推演时存在误差累积和非物理解漂移问题。现有混合方法多依赖人工实现，仅适用于低维基准测试。本文提出基于OpenFOAM的XRePIT（eXtensible Residual-based Physics-informed Transfer learning）框架，具备快速、鲁棒、可扩展特性。不同于以往手动实现（如RePIT），XRePIT采用全自动化开源流程，根据监测残差阈值动态切换神经代理与传统数值求解器（OpenFOAM）。以三维浮力驱动流为测试基准，结果表明该残差引导耦合可实现稳定长时仿真，突破单一代理模型的稳定性极限。实验显示，混合循环实现最高2.91倍墙钟加速，相对L2误差维持在O(1E-03)量级。通过引入基于有限体积的傅里叶神经算子（FVFNO），验证了残差保护机制对底层神经架构的无关性，证明框架具有强可扩展性。本研究提供了一种可部署的3D非定常流动快速、鲁棒、自动化的混合仿真方法。
原文摘要 · Abstract (English)
Autoregressive neural surrogates offer computational acceleration for fluid dynamics but inherently suffer from error accumulation and non-physical drift during long-term rollouts. Although hybrid strategies combining surrogate models and physics-based solvers have been proposed, they are limited to manual implementations for low-dimensional benchmarks. In this study, we propose an OpenFOAM-based hybrid framework, XRePIT (eXtensible Residual-based Physics-nformed Transfer learning), characterized by its fastness, robustness, and scalability. Unlike prior manual implementations (e.g., RePIT), XRePIT integrates a fully automated open-source workflow that manages the state transition between a neural surrogate and a traditional numerical solver (OpenFOAM) based on a monitored residual threshold. Using 3D buoyancy-driven flow as a testbed, we demonstrate that this residual-guided coupling enables stable long-term simulation-ell beyond the stability horizon of standalone surrogates. Our results indicate that the hybrid loop achieves up to 2.91x wall-clock acceleration while maintaining relative L2 errors within O(1E-03) Furthermore, we benchmark the framework's extensibility by introducing a finite-volume-based Fourier neural operator (FVFNO), confirming that the stabilizing effect of the residual guardrail is agnostic to the underlying neural architecture. This study provides a deployable methodology for fast, robust, and automated hybrid simulation in 3D unsteady flow.
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