2021
DOI: 10.48550/arxiv.2104.01042
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Assessment of machine learning methods for state-to-state approaches

Abstract: It is well known that numerical simulations of high-speed reacting flows, in the framework of stateto-state formulations, are the most detailed but also often prohibitively computationally expensive. In this work, we start to investigate the possibilities offered by the use of machine learning methods for state-to-state approaches to alleviate such burden. In this regard, several tasks have been identified. Firstly, we assessed the potential of state-ofthe-art data-driven regression models based on machine lea… Show more

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