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

2026-06-11

AI Summary

This work addresses the challenge of accurately and physically consistently capturing sharp gradients such as shock waves in hypersonic flow fields, which are poorly resolved by conventional reduced-order models or neural surrogates. To this end, the authors propose an end-to-end fully GPU-accelerated workflow that leverages the differentiable high-fidelity solver JAX-Fluids for efficient data generation and introduces a residual-driven, physics-aware refinement mechanism. The resulting neural surrogate is trained using only mesh coordinates and input parameters, significantly reducing residuals of the governing equations while improving the physical fidelity of predicted shock locations and strengths. The method demonstrates strong generalization and reliability even under out-of-distribution operating conditions.

Abstract

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.

Problem

• hypersonic flows
• shock waves
• neural emulators
• physical consistency
• steep gradients

Innovation

• GPU-based workflow
• differentiable simulation
• physics-informed neural emulator
• residual-based refinement
• hypersonic flow