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A Data-Driven Sparse-Learning Approach to Model Reduction in Chemical Reaction Networks

Fri Dec 08 09:00 AM -- 09:20 AM (PST) @

In this paper, we propose an optimization-based sparse learning approach to iden- tify 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 compu- tational 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.

Author Information

FARSHAD HARIRCHI (University of Michigan)

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