An Information-Theoretical Perspective on Complex Dynamical Systems
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Complexity".
Deadline for manuscript submissions: 30 April 2024 | Viewed by 176
Special Issue Editors
Interests: filtering; multiscale data assimilation; statistical control; data-driven models for turbulent systems
Special Issue Information
Dear Colleagues,
Complex dynamical systems are ubiquitous in various science and engineering fields. Key issues to consider include their basic mathematical structural properties and qualitative features, their statistical prediction, uncertainty quantification (UQ) and sensitivity, their data assimilation/filtering, and the inevitable model errors that arise in approximating such complex systems. These model errors arise through both the curse of small ensemble size for large systems and the lack of physical understanding. Information theory provides an objective, unbiased way to evaluate model errors. Strategies for effective modeling and prediction require blending ideas from information theory, Bayesian statistics, and statistical physics in an emerging paradigm for these grand challenges, including extreme event prediction.
This Special Issue aims at introducing new insights and approaches for advancing the study of general complex systems. Topics of interest for this Special Issue include, but are not limited to, the following themes:
- Building suitable unambiguous mathematical models for statistical/stochastic prediction, parameter estimation, and uncertainty quantification;
- Improved understanding of model-based ensemble forecast using information theory and judicious comprehensive mathematical tools;
- New data-driven and machine learning strategies to characterize the dynamical and statistical features of high-dimensional complex systems;
- Emerging development of multiscale algorithms for filtering/data assimilation and state estimation for complex systems and real-world applications;
- Information barriers and improving the skill with model errors for state estimation and prediction.
Dr. Di Qi
Prof. Dr. José F. F. Mendes
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- complex dynamical systems
- stochastic modeling
- data assimilation
- information theory
- data-driven methods
- extreme events
