Data-driven, variational model reduction of high-dimensional reaction networks

DOI10.1016/j.jcp.2019.108997zbMath1453.62637arXiv1807.05319OpenAlexW2977538538WikidataQ127179845 ScholiaQ127179845MaRDI QIDQ2222683

Markos A. Katsoulakis, Pedro Vilanova

Publication date: 27 January 2021

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1807.05319

zbMATH Keywords

Markov processesmodel reductionvariational inferencereaction networksscientific machine learningpathwise Fisher information matrix

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Stochastic ordinary differential equations (aspects of stochastic analysis) (60H10) Learning and adaptive systems in artificial intelligence (68T05) Classical flows, reactions, etc. in chemistry (92E20) Time series analysis of dynamical systems (37M10)

Related Items (3)

Discovery of slow variables in a class of multiscale stochastic systems via neural networks ⋮ Model order reduction of positive real systems based on mixed Gramian balanced truncation with error bounds ⋮ Towards Confident Bayesian Parameter Estimation in Stochastic Chemical Kinetics

Uses Software

• CHEMKIN
• TChem
• PRMLT

Cites Work

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