Title: A Data-Driven Sparse-Learning Approach to Model Reduction in Chemical Reaction Networks,
Abstract: 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.