TY - JOUR
T1 - Data-driven, variational model reduction of high-dimensional reaction networks
AU - Katsoulakis, Markos A.
AU - Vilanova, Pedro
N1 - Publisher Copyright:
© 2019
PY - 2020/1/15
Y1 - 2020/1/15
N2 - In this work we present new scalable, information theory-based variational methods for the efficient model reduction of high-dimensional deterministic and stochastic reaction networks. The proposed methodology combines, (a) information theoretic tools for sensitivity analysis that allow us to identify the proper coarse variables of the reaction network, with (b) variational approximate inference methods for training a best-fit reduced model. This approach takes advantage of both physicochemical modeling and data-based approaches and allows to construct optimal parameterized reduced dynamics in the number of variables, reactions and parameters, while controlling the information loss due to the reduction. We demonstrate the effectiveness of our model reduction method on several complex, high-dimensional chemical reaction networks arising in biochemistry.
AB - In this work we present new scalable, information theory-based variational methods for the efficient model reduction of high-dimensional deterministic and stochastic reaction networks. The proposed methodology combines, (a) information theoretic tools for sensitivity analysis that allow us to identify the proper coarse variables of the reaction network, with (b) variational approximate inference methods for training a best-fit reduced model. This approach takes advantage of both physicochemical modeling and data-based approaches and allows to construct optimal parameterized reduced dynamics in the number of variables, reactions and parameters, while controlling the information loss due to the reduction. We demonstrate the effectiveness of our model reduction method on several complex, high-dimensional chemical reaction networks arising in biochemistry.
KW - Markov processes
KW - Model reduction
KW - Pathwise Fisher information matrix
KW - Reaction Networks
KW - Scientific Machine Learning
KW - Variational Inference
UR - http://www.scopus.com/inward/record.url?scp=85074477984&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85074477984&partnerID=8YFLogxK
U2 - 10.1016/j.jcp.2019.108997
DO - 10.1016/j.jcp.2019.108997
M3 - Article
AN - SCOPUS:85074477984
SN - 0021-9991
VL - 401
JO - Journal of Computational Physics
JF - Journal of Computational Physics
M1 - 108997
ER -