Abstract
Earlier we have explored different deterministic, stochastic and metaheuristic methods for identifying parameters of heterogeneous reactions for diffusion dominated and reaction dominated regimes [2,3,4,5]. Pore scale reactive transport was studied, breakthrough curves were the additional information used in identifying the parameters in Henry or Langmuir isotherms. All methods were time consuming, requiring multiple solution of the direct problem. Various surrogate models are used in the literature to reduce the computational burden related to parameter identification problems. In this paper we explore surrogate models based on neural network, Gaussian process, and cross approximation approaches. We also extend the number of the sought parameters. The achieved accuracy and the performance of the surrogate models were studied.
DF and OI were supported by BMBF under grant 05M20AMD ML-MORE, VVG was supported by Ministry of Education and Science of the Russian Federation under grant No. FSRG-2023-0025, and IO was supported via von Humboldt Research Award.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Fokina, D., Toktaliev, P., Iliev, O., Oseledets, I.: Machine learning methods for prediction of breakthrough curves in reactive porous media (2023)
Grigoriev, V.V., Iliev, O., Vabishchevich, P.N.: Computational identification of adsorption and desorption parameters for pore scale transport in periodic porous media. J. Comput. Appl. Math. 370, 112661 (2020)
Grigoriev, V.V., Iliev, O., Vabishchevich, P.N.: Computational identification of adsorption and desorption parameters for pore scale transport in random porous media. In: Lirkov, I., Margenov, S. (eds.) LSSC 2019. LNCS, vol. 11958, pp. 115–122. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-41032-2_12
Grigoriev, V.V., Iliev, O., Vabishchevich, P.N.: On parameter identification for reaction-dominated pore-scale reactive transport using modified bee colony algorithm. Algorithms 15(1), 15 (2022)
Grigoriev, V.V., Vabishchevich, P.N.: Bayesian estimation of adsorption and desorption parameters for pore scale transport. Mathematics 9(16), 1974 (2021)
Williams, C., Rasmussen, C.: Gaussian processes for regression. In: Touretzky, D., Mozer, M., Hasselmo, M. (eds.) Advances in Neural Information Processing Systems, vol. 8. MIT Press (1995)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Fokina, D., Grigoriev, V.V., Iliev, O., Oseledets, I. (2024). Machine Learning Algorithms for Parameter Identification for Reactive Flow in Porous Media. In: Lirkov, I., Margenov, S. (eds) Large-Scale Scientific Computations. LSSC 2023. Lecture Notes in Computer Science, vol 13952. Springer, Cham. https://doi.org/10.1007/978-3-031-56208-2_8
Download citation
DOI: https://doi.org/10.1007/978-3-031-56208-2_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-56207-5
Online ISBN: 978-3-031-56208-2
eBook Packages: Computer ScienceComputer Science (R0)