Young Researchers Workshop: Ki-Net 2012-2019

Information-based variational model reduction of high-dimensional reaction networks

Pedro Vilanova

NJIT

In this talk I will present new scalable, information theory-based variational methods for the efficient model reduction of high-dimensional deterministic and stochastic reaction networks. 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.