2017
DOI: 10.48550/arxiv.1712.06281
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A New Data-Driven Sparse-Learning Approach to Study Chemical Reaction Networks

Abstract: Chemical kinetic mechanisms can be represented by sets of elementary reactions that are easily translated into mathematical terms using physicochemical relationships. The schematic representation of reactions captures the interactions between reacting species and products. Determining the minimal chemical interactions underlying the dynamic behavior of systems is a major task. In this paper, we introduce a novel approach for the identification of the influential reactions in chemical reaction networks for comb… Show more

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