Information Theoretic Tools for Parameter Fitting in Coarse Grained Models

We study the application of information theoretic tools for model reduction in the case of systems driven by stochastic dynamics out of equilibrium. The model/dimension reduction is considered by proposing parametrized coarse grained dynamics and finding the optimal parameter set for which the relative entropy rate with respect to the atomistic dynamics is minimized. The minimization problem leads to a generalization of the force matching methods to non equilibrium systems. A multiplicative noise example reveals the importance of the diffusion coefficient in the optimization problem.

Conference/Event Name
Advances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2015)

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