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. 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. 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. 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. 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.

翻译：高保真度且低计算成本地解析复杂物理现象的能力，是现代工程应对关键挑战的核心。以高超声速流为例，精确预测完整流场拓扑结构（尤其是激波位置与强度）至关重要。然而，超声速与高超声速流场中剧烈的状态梯度，使得传统降阶模型及神经仿真器难以在工业相关应用中保持物理一致性。为此，我们提出一种基于GPU的全流程方案，该方案将加速数据生成与神经网络仿真器训练相结合，并融入不确定性量化与物理感知优化。该流程依赖于可微分高保真求解器（JAX-Fluids），用于快速数据集构建及基于残差的神经网络仿真器改进，以增强物理一致性。基于该框架，我们首先展示一系列模型架构并分析其缩放行为，揭示其优势与局限。继而证明，基于残差的优化能够利用仅有网格与输入参数的算例进行训练，显著降低残差并提升物理一致性。可微分仿真与残差优化相结合，最终生成在训练分布外仍保持可靠性的物理仿真器——这是将代理模型部署至真实工程设计循环的关键要求。