Accelerating hypersonic reentry simulations using deep learning-based hybridization (with guarantees)

TL;DR

This paper presents a hybrid simulation approach combining traditional fluid dynamics with neural networks to accelerate hypersonic reentry simulations, achieving significant speedups while maintaining accuracy guarantees.

Contribution

It introduces a novel hybrid simulation framework that couples fluid dynamics with neural networks for chemical reactions, ensuring accuracy guarantees in hypersonic reentry modeling.

Findings

Achieved acceleration factors of 10 to 18.6 times.

Developed methodologies for accuracy guarantees in hybrid simulations.

Demonstrated practical application in hypersonic reentry scenarios.

Abstract

In this paper, we are interested in the acceleration of numerical simulations. We focus on a hypersonic planetary reentry problem whose simulation involves coupling fluid dynamics and chemical reactions. Simulating chemical reactions takes most of the computational time but, on the other hand, cannot be avoided to obtain accurate predictions. We face a trade-off between cost-efficiency and accuracy: the simulation code has to be sufficiently efficient to be used in an operational context but accurate enough to predict the phenomenon faithfully. To tackle this trade-off, we design a hybrid simulation code coupling a traditional fluid dynamic solver with a neural network approximating the chemical reactions. We rely on their power in terms of accuracy and dimension reduction when applied in a big data context and on their efficiency stemming from their matrix-vector structure to achieve important acceleration factors ($\times 10$ to $\times 18.6$). This paper aims to explain how we design such cost-effective hybrid simulation codes in practice. Above all, we describe methodologies to ensure accuracy guarantees, allowing us to go beyond traditional surrogate modeling and to use these codes as references.