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Information-based Variational Model Reduction of high-dimensional Reaction Networks. (arXiv:1807.05319v1 [math.NA])
来源于:arXiv
In this work we present new scalable, information theory-based variational
methods for the efficient model reduction of high-dimensional 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. The overall approach takes advantage of both
physicochemical modeling and data-based approaches and allows to construct
optimal parameterized reduced dynamics in the number of species, 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 biochemical reaction networks from the recent literature. 查看全文>>