A Data-Driven Sparse-Learning Approach to Model Reduction in Chemical Reaction Networks
This work addresses model reduction for chemical reaction networks, which is incremental as it builds on existing sparse learning methods.
The paper tackles the problem of identifying influential reactions in chemical reaction networks by proposing an optimization-based sparse learning approach, resulting in a reduced mechanism that captures important structural properties with moderate computational load.
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.