Second Workshop on Information Theory and the Earth Sciences
May 16-19, 2018, Environmental Hydraulic Institute (IHCantabria),
Information Theoretic analyses are essentially general in nature and can be applied to all parts of the scientific endeavor: complex systems, models of those systems, observational data, and the synthesis of these things. Rooted firmly in mathematics and statistical theory, IT provides a compelling basis for expanding upon tools and methods that typically make simplifying assumptions of linearity and Gaussianity to address problems of inference. Because of this, IT has the potential to help us understand emergent behavior of complex systems in ways that more traditional analyses cannot (e.g., synergistic information, extreme non-linearity, networks etc.). Additionally, IT allows us to study any and all parts of a system (real or modeled) under a common dynamical framework, so that, in principle, no a priori assumptions must be made to understand relationships between a large number of diverse dynamical processes.
Goals: The goals of this workshop are to promote the innovative use of Information Theoretic concepts in the service of discovery, modeling and decision-making in the Earth and Environmental Sciences and, through high level presentations and open discussion, to inspire revolutionary advances in the theories of modeling, learning, inference, and diagnostic evaluation. The workshop focus is on scientific sharing, discussion, debate and fun. The purpose is to foster collaboration among a growing community of Earth scientists who use or are interested in this broad and diverse mathematical theory.
Topics presented in may include, but are not limited to the following:
- How to properly include in models the things we already know (e.g., all physical laws, and not just conservation of mass and energy, etc.).
- How to evaluate the usefulness and robustness of data and models for a given task in a generalized way (i.e., how to establish their task-relevant information content)
- How to evaluate the appropriateness of models given the data and the purpose (i.e., how to establish their generality, parsimony, flexibility, etc.)?
- How to evaluate the interplay of data-, model structure- and predictive “uncertainty” (i.e., the flow of information from data through models to decision-makers)?
- How to learn from the encounter of models and data (i.e., how to detect, diagnose and correct model structural errors)?
The workshop is a continuation of the 2016 Workshop called ‘Information Theory and the Earth Sciences’ held at the Schneefernerhaus, Germany (see attached excerpt from the AGU HS Newsletter July 2017), however participation in that previous workshop is not necessary, given that the objective of this follow-on workshop is to foster and develop continued growth and participation in the field.
Format: The workshop will include a number of targeted keynotes, oral and poster presentations by the participants, but will mostly consist of moderated discussion groups focused around specific pre-prepared questions/issues. We encourage every participant to submit an abstract related to the topic of information theory (deadline to be announced, roughly end January).
For newcomers to the field (and also for “experienced” practitioners), we offer a pre-workshop 1-day tutorial on the basic concepts of Information Theory ( Wed 16th May ), and additional tutorials during the days of the workshop.
This workshop is in collaboration with the Entropy Journal:
‘Entropy is an open access journal which maintains a rigorous and fast peer-review system with a median publication time of 53.5 days from submission to publication online. It was established in 1999 and is monthly published by MDPI.It is fully covered by the leading indexing and abstracting services, including Scopus and SCIE (Web of Science), Google Scholar and MathSciNet. The official Impact Factor for Entropy is 1.821 (2016). http://www.mdpi.com/journal/entropy’’
(Environmental Hydraulics Institute of Cantabria “IHCantabria”, Santander, Spain;
Imperial College London, UK; Bristol University, UK)
(KIT Karlsruhe Institute of Technology, Germany)
(University of Arizona, U.S.)
(NASA Goddard, U.S.),
(Swedish Meteorological and Hydrological Institute, SMHI, Sweden)
(University of British Columbia, Canada)
(Aachen University, Germany)
(Arizona State University, U.S.)
(Univ. of Illinois, Urbana-Champaign)
(Helmholtz Center for Environmental Research, Germany)
(University of Saskatchewan, Canada),
(Spanish National Research Council, CSIC, Spain)