A data-driven reduced-order model for stiff chemical kinetics using dynamics-informed training

A data-based reduced-order model (ROM) is developed to accelerate the time integration of stiff chemically reacting systems by effectively removing the stiffness arising from a wide spectrum of chemical time scales. Specifically, the objective of this work is to develop a ROM that acts as a non-stif...

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Bibliographic Details

Published in:	Energy and AI
Main Authors:	Vijayamanikandan Vijayarangan, Harshavardhana A. Uranakara, Shivam Barwey, Riccardo Malpica Galassi, Mohammad Rafi Malik, Mauro Valorani, Venkat Raman, Hong G. Im
Format:	Article
Language:	English
Published:	Elsevier 2024-01-01
Subjects:	Stiff system Chemical kinetics Reacting flows Autoencoders Neural ODE
Online Access:	http://www.sciencedirect.com/science/article/pii/S2666546823000976