Learning chemical reaction networks from trajectory data

under review
  • In this paper, we develop a data-driven numerical method to learn chemical reaction networks from trajectory data. Modeling the reaction system as a continuous-time Markov chain, our method learns the propensity functions of the system with predetermined basis functions by maximizing the likelihood function of the trajectory data under l^1 sparse regularization. We demonstrate our method with numerical examples, and we perform asymptotic analysis of the proposed learning procedure in the infinite-data limit.

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Metadaten
Author:Wei Zhang, Stefan Klus, Tim Conrad, Christof Schütte
Document Type:Article
Parent Title (English):SIAM Journal on Applied Dynamical Systems (SIADS)
Year of first publication:2019
ArXiv Id:http://arxiv.org/abs/1902.04920