标题： Diff-FlowFSI：一种针对高保真湍流和流固耦合模拟优化的GPU差异化计算流体力学平台 显示英文标题

标题： Diff-FlowFSI: A GPU-Optimized Differentiable CFD Platform for High-Fidelity Turbulence and FSI Simulations

摘要： 湍流流动和流固相互作用（FSI）在科学和工程应用中无处不在，但由于强非线性、多尺度相互作用以及高计算需求，其准确且高效的模拟仍然是一个重大挑战。 尽管传统的计算流体力学（CFD）求解器有效，但在逆向建模、优化和数据同化等任务中的可扩展性和适应性方面仍存在问题。 机器学习（ML）的最新进展激发了混合建模方法，这些方法结合神经网络与基于物理的求解器以增强泛化能力并捕捉未解析的动力学。 然而，实现这种集成需要不仅在物理上精确，而且可微分且GPU高效的求解器。 在这项工作中，我们介绍了Diff-FlowFSI，这是一种用于高保真湍流和FSI模拟的GPU加速、完全可微分的CFD平台。 使用JAX实现，Diff-FlowFSI结合了向量化有限体积求解器和浸入边界法，以处理复杂几何形状和流固耦合。 该平台支持GPU加速的快速前向模拟，支持基于梯度的反问题的自动微分，并无缝集成深度学习组件以实现混合神经-CFD建模。 我们在一系列基准湍流和FSI问题中验证了Diff-FlowFSI，展示了它在物理与机器学习交叉领域加速科学计算的能力。 显示英文摘要

摘要： Turbulent flows and fluid-structure interactions (FSI) are ubiquitous in scientific and engineering applications, but their accurate and efficient simulation remains a major challenge due to strong nonlinearities, multiscale interactions, and high computational demands. Traditional CFD solvers, though effective, struggle with scalability and adaptability for tasks such as inverse modeling, optimization, and data assimilation. Recent advances in machine learning (ML) have inspired hybrid modeling approaches that integrate neural networks with physics-based solvers to enhance generalization and capture unresolved dynamics. However, realizing this integration requires solvers that are not only physically accurate but also differentiable and GPU-efficient. In this work, we introduce Diff-FlowFSI, a GPU-accelerated, fully differentiable CFD platform designed for high-fidelity turbulence and FSI simulations. Implemented in JAX, Diff-FlowFSI features a vectorized finite volume solver combined with the immersed boundary method to handle complex geometries and fluid-structure coupling. The platform enables GPU-enabled fast forward simulations, supports automatic differentiation for gradient-based inverse problems, and integrates seamlessly with deep learning components for hybrid neural-CFD modeling. We validate Diff-FlowFSI across a series of benchmark turbulence and FSI problems, demonstrating its capability to accelerate scientific computing at the intersection of physics and machine learning.