Information Theory, Data Assimilation and Stochastics for Multiscale Nonlinear Systems

Dear Colleagues, 

Complex multiscale nonlinear stochastic dynamical systems are ubiquitous in geoscience, engineering, and neural and material sciences. For many such systems, due particularly to their high dimensionality and partial observability, it remains a grand challenge in contemporary science and engineering to understand the underlying dynamical mechanisms and to predict their short-term and long-term behaviors. At the same time, cross-disciplinary efforts on various key issues such as coarse-grained dynamics, statistics inference, uncertainty quantification, data assimilation, and extreme-event detection, have collectively led to both more efficient algorithms and improved analytical framework. Moving forward, it is also expected that a judicious use of available data in combination with effective reduced-order models that exploit physics, dynamics as well as statistics constraints will play an increasing role in the theoretic analysis and practical manipulations of such systems. 

The main focus of this Special Issue will be on the state-of-the-art advancements concerning information theory, data assimilation and stochastic/reduced-order modeling of complex multiscale nonlinear systems. We welcome contributions on topics such as Bayesian inferences and sampling, rigorous analysis of the dynamical properties, efficient numerical algorithms, effective low-dimensional surrogate models, and relevant applications. 

Dr. Nan Chen
Dr. Honghu Liu
Dr. Evelyn Lunasin
Guest Editors

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