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Register a new userA Data-Driven Sparse-Learning Approach to Model Reduction in Chemical Reaction Networks
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Added by
Farshad Harirchi
Publication date
2017
fields
Informatics Engineering
and research's language is
English
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In this paper, we propose an optimization-based sparse learning approach to identify the set of most influential reactions in a chemical reaction network. This reduced set of reactions is then employed to construct a reduced chemical reaction mechanism, which is relevant to chemical interaction network modeling. The problem of identifying influential reactions is first formulated as a mixed-integer quadratic program, and then a relaxation method is leveraged to reduce the computational complexity of our approach. Qualitative and quantitative validation of the sparse encoding approach demonstrates that the model captures important network structural properties with moderate computational load.
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