@article{6a8fa0db78fe4bdaab4c9ee9a34aa57d,
title = "Learning chemical reaction networks from trajectory data",
abstract = "We develop a data-driven method to learn chemical reaction networks from trajectory data. Modeling the reaction system as a continuous-time Markov chain and assuming the system is fully observed, our method learns the propensity functions of the system with predetermined basis functions by maximizing the likelihood function of the trajectory data under l1 sparse regularization. We demonstrate our method with numerical examples using synthetic data and carry out an asymptotic analysis of the proposed learning procedure in the infinite-data limit.",
keywords = "Asymptotic analysis, Chemical reactions, Data-driven methods, Inverse problems, L sparse optimization",
author = "Wei Zhang and Stefan Klus and Tim Conrad and Christof Sch{\"u}tte",
note = "Funding Information: ∗Received by the editors June 3, 2019; accepted for publication (in revised form) by L. Deville September 24, 2019; published electronically November 12, 2019. https://doi.org/10.1137/19M1265880 Funding: This work was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany{\textquoteright}s Excellence Strategy, the Berlin Mathematics Research Center, MATH+ (EXC-2046/1, project 390685689), and the Einstein Center of Mathematics (ECMath) through project CH21. †Zuse Institute Berlin, D-14195 Berlin, Germany (
[email protected],
[email protected],
[email protected]). ‡Department of Mathematics and Computer Science, Freie Universit{\"a}t Berlin, D-14195 Berlin, Germany (
[email protected]). Publisher Copyright: {\textcopyright} 2019 Society for Industrial and Applied Mathematics",
year = "2019",
doi = "10.1137/19M1265880",
language = "English",
volume = "18",
pages = "2000--2046",
journal = "SIAM Journal on Applied Dynamical Systems",
issn = "1536-0040",
publisher = "Society of Industrial and Applied Mathematics",
number = "4",
}