JAX-Shock: 一种可微、GPU加速的冲击捕捉神经求解器用于压缩流体模拟
JAX-Shock: A Differentiable, GPU-Accelerated, Shock-Capturing Neural Solver for Compressible Flow Simulation
AI总结：
JAX-Shock是一种基于JAX的可微神经求解器，通过高阶WENO重建和神经通量模块提升压缩流体中冲击-固体相互作用的模拟精度与泛化能力。
摘要：
理解冲击-固体相互作用仍然是压缩流体动力学中的核心挑战。我们提出了JAX-Shock：一种完全可微、GPU加速的高阶冲击捕捉求解器，用于高效模拟压缩纳维-斯托克斯方程。该框架完全基于JAX构建，利用自动微分技术实现基于梯度的优化、参数推断以及深度学习增强模型的端到端训练。求解器集成了五阶WENO重建与HLLC通量，以高保真度解析冲击和不连续性。为了处理复杂几何形状，实现了浸入边界方法，以准确表示固体界面在压缩流场中的位置。此外，我们引入了一个神经通量模块，通过数据驱动的修正来增强数值通量，显著提高了精度和泛化能力。JAX-Shock还支持序列到序列学习用于冲击相互作用预测以及反向模式推断以从数据中识别关键物理参数。与纯数据驱动方法相比，JAX-Shock在增强泛化能力的同时保持了物理一致性。该框架为不同可微物理、基于学习的建模和逆设计在由复杂冲击-固体相互作用主导的压缩流场中建立了灵活的平台。
英文摘要：
Understanding shock-solid interactions remains a central challenge in compressiblefluiddynamics. WepresentJAX-Shock: afully-differentiable,GPU-accelerated, high-order shock-capturing solver for efficient simulation of the compressible Navier-Stokes equations. Built entirely in JAX, the framework leverages automatic differentiation to enable gradient-based optimization, parameter inference, and end-to-end training of deep learning-augmented models. The solver integrates fifth-order WENO reconstruction with an HLLC flux to resolve shocks and discontinuities with high fidelity. To handle complex geometries, an immersed boundary method is implemented for accurate representation of solid interfaces within the compressible flow field. In addition, we introduce a neural flux module trained to augment the numerical fluxes with data-driven corrections, significantly improving accuracy and generalization. JAX-Shock also supports sequence-to-sequence learning for shock interaction prediction and reverse-mode inference to identify key physical parameters from data. Compared with purely data-driven approaches, JAX-Shock enhances generalization while preserving physical consistency. The framework establishes a flexible platform for differentiable physics, learning-based modeling, and inverse design in compressible flow regimes dominated by complex shock-solid interactions.
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