# Data-driven framework for input/output lookup tables reduction: Application to hypersonic flows in chemical non-equilibrium

arXiv: https://arxiv.org/abs/2210.04269 · HTML: https://mlsanity.com/paper/2210.04269/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 61 references that are in the map.
- First posted on arXiv 9 Oct 2022.

## Builds on (most cited first, 1 of 1)

- [A Tutorial on Principal Component Analysis](https://mlsanity.com/paper/1404.1100/) (2014)

## Built on by (most cited first, 1 of 1)

- [RONAALP: Reduced-Order Nonlinear Approximation with Active Learning Procedure](https://mlsanity.com/paper/2311.10550/) (2023)

## Abstract (arXiv)

In this paper, we present a novel model-agnostic machine learning technique to extract a reduced thermochemical model for reacting hypersonic flows simulation. A first simulation gathers all relevant thermodynamic states and the corresponding gas properties via a given model. The states are embedded in a low-dimensional space and clustered to identify regions with different levels of thermochemical (non)-equilibrium. Then, a surrogate surface from the reduced cluster-space to the output space is generated using radial-basis-function networks. The method is validated and benchmarked on a simulation of a hypersonic flat-plate boundary layer with finite-rate chemistry. The gas properties of the reactive air mixture are initially modeled using the open-source Mutation++ library. Substituting Mutation++ with the light-weight, machine-learned alternative improves the performance of the solver by 50% while maintaining overall accuracy.