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Path-space information metrics for uncertainty quantification and coarse-graining of molecular systems

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SDB - Stochastic dynamical systems in biology: numerical methods and applications

We present path-space, information theory-based, sensitivity analysis, uncertainty quantification and variational inference methods for complex high-dimensional stochastic dynamics, including chemical reaction networks with hundreds of parameters, Langevin-type equations and lattice kinetic Monte Carlo. We establish their connections with goal-oriented methods in terms of new, sharp, uncertainty quantification inequalities that scale appropriately at both long times and for high dimensional state and parameter space.   The combination of proposed methodologies is capable to (a) tackle non-equilibrium processes, typically associated with coupled physicochemical mechanisms or boundary conditions, such as reaction-diffusion problems, and where even steady states are unknown altogether, e.g. do not have a Gibbs structure. The path-wise information theory tools,  (b) yield a surprisingly simple, tractable and easy-to-implement approach to quantify and rank parameter sensitivities, as well as  (c) provide reliable parameterizations for coarse-grained molecular systems based on fine-scale data, and rational model selection through path-space (dynamics-based) variational inference methods.

This talk is part of the Isaac Newton Institute Seminar Series series.

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