Author:
Mal’sagov M. Yu.,Mikhal’chenko E. V.,Karandashev I. M.,Nikitin V. F.
Subject
General Physics and Astronomy,Energy Engineering and Power Technology,Fuel Technology,General Chemical Engineering,General Chemistry
Reference8 articles.
1. W. Y. Peng and N. H. Pinkowski, “Efficient and Accurate Time-Integration of Combustion Chemical Kinetics Using Artificial Neural Networks," https://cs229.stanford.edu/ proj2017/final-reports/5241836.pdf (2017).
2. A. J. Sharma, R. F. Johnson, D. A. Kessler, and A. Moses, “Deep Learning for Scalable Chemical Kinetics," in Proc. AIAA Scitech 2020 Forum, Orlando, January, 6–10, 2020, AIAA 2020-0181; DOI: 10.2514/6.2020-0181.
3. W. Ji and S. Deng, “KiNet: A Deep Neural Network Representation of Chemical Kinetics," https://arxiv.org/abs/2108.00455 (2021).
4. V. B. Betelin, B. V. Kryzhanovsky, N. N. Smirnov, et al., “Neural Network Approach to Solve Gas Dynamics Problems with Chemical Transformations," Acta Astronaut. 180, 58–65 (2020); DOI: 10.1016/j.actaastro.2020.11.058.
5. V. F. Nikitin, I. M. Karandashev, M. Yu. Malsagov, and E. V. Mikhalchenko, “Approach to Combustion Calculation Using Neural Network," Acta Astronaut. 194, 376–382 (2022); DOI: 10.1016/j.actaastro.2021.10.034.