Dynamics and Information Theory in Phase Space
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".
Deadline for manuscript submissions: closed (31 July 2021) | Viewed by 481
Special Issue Editors
Interests: attophoto chemistry; molecular logic; surprisal analysis; systems biology; molecular reaction dynamics
Interests: photoinduced quantum dynamics; attochemistry; molecular parallel computing; nanosystems.
Interests: ultrafast photochemistry; algebraic approach; information theory; non-adiabatic dynamics; molecular spectroscopy
Special Issue Information
Dear Colleagues,
In this age of big data, a quantitative compact summary of the essential features of the system and how to extract them is needed throughout the sciences. This is especially the case when the system is evolving in time and one seeks to also describe its history. In the more quantitative sciences, the time course requires two ingredients: the law of change and the initial state of the system. The Laplace demon was an early practitioner. The demon had a very definite initial state. Beginning with Boltzmann and Gibbs, phase space is where the incomplete specification of the initial conditions lives and where it is convenient to describe how the system evolves. We need to know how to infer the initial state and how to propagate it in time. As the equation of motion, Boltzmann used a kinetic scheme while Gibbs used classical mechanics. The exponential growth of activities in quantum technologies provides us with novel tools for exploring the sampling of the phase space of complex systems. So, two key aspects of our subject are what methodology generates the time translation and are there ways of learning it. Next, we need to deal with reduction of the size of the problem. Lastly, we have custom-made approaches that have been tailored to the class of systems of interest (e.g., protein folding; what is the reaction coordinate; information transduction in a network). As the number of degrees of freedom grows, computational aspects become of increasing importance. What is the holy grail? Most likely, it is to converge on the function (operator) that generates the time displacement on a reduced level of description. Thermodynamics tell us that under well-defined conditions this is the free energy. How do we generalize it?
Prof. Dr. Raphael Levine
Prof. Dr. Françoise Remacle
Dr. Ksenia Komarova
Guest Editors
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Keywords
- algebraic description of phase space
- maximal entropy formalism
- statistical mechanics of learning
- free energy landscape
- barrier crossing dynamics
- dynamics on networks
- coherent control
- dynamical groups
- Markov models
- computing with observables
- dimensionality reduction
- reduced descriptions
- clustering
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