A fully GPU-based workflow for building physics emulators of hypersonic flows

构建高超音速流物理模拟器的全 GPU 工作流

The ability to resolve complex physical phenomena with high fidelity and at low computational cost is central to addressing key challenges in modern engineering. A prime example lies in hypersonic flows, where the precise prediction of the full flowfield topology, in particular with respect to shock wave location and intensity, is critical.

以高保真度、低计算成本解析复杂物理现象的能力，是应对现代工程关键挑战的核心。高超音速流就是一个典型的例子，在这种流场中，对完整流场拓扑结构的精确预测（特别是关于激波位置和强度的预测）至关重要。

Yet supersonic and hypersonic flows continue to be a stumbling block for traditional reduced-order models and neural emulators that struggle to capture steep gradients in flow states with physical consistency in applications of industrial relevance.

然而，超音速和高超音速流仍然是传统降阶模型和神经模拟器的绊脚石，这些模型在工业相关应用中，难以在保持物理一致性的同时捕捉流态中的陡峭梯度。

To that end, we introduce a fully GPU based workflow that integrates accelerated data generation with the training of neural emulators augmented by uncertainty quantification and physics-aware refinement. Our workflow is enabled by a differentiable high-fidelity solver (JAX-Fluids) which we employ for rapid dataset creation and residual-based improvement of the neural emulator to enhance physical consistency.

为此，我们引入了一种基于全 GPU 的工作流，该工作流将加速数据生成与神经模拟器的训练相结合，并辅以不确定性量化和物理感知细化。我们的工作流由一个可微分的高保真求解器（JAX-Fluids）支持，我们利用它进行快速数据集创建，并通过基于残差的改进来增强神经模拟器的物理一致性。

Building on this framework, we first present a suite of model architectures and analyze their scaling behavior to expose their strengths and shortcomings. We then show that residual-based refinement enables training on cases where only mesh and input parameters are available, substantially reducing residuals and improving physical consistency.

在此框架基础上，我们首先展示了一套模型架构，并分析了它们的缩放行为，以揭示其优势和不足。随后我们证明，基于残差的细化使得在仅有网格和输入参数的情况下也能进行训练，从而显著降低了残差并提高了物理一致性。

Together, differentiable simulation and residual-based refinement yield physics emulators that remain reliable beyond their training distribution, a key requirement for deploying surrogates in real-world engineering design loops.

总之，可微分模拟和基于残差的细化共同产生了在训练分布之外依然可靠的物理模拟器，这是在现实工程设计循环中部署代理模型的关键要求。