Complexity in Physics and Chemistry


Harnessing Spin Chain Methods To Derive Simple Predictive Models Of Complex Stochastic Processes

Stochastic processes are ubiquitous throughout the quantitative sciences and their study is of great interest across disciplinary boundaries. The complexity of stochastic process has been related to the minimal amount of memory about its past which one requires in order to predict its future [1]. Recent studies have shown that quantum hidden Markov models can further reduce this memory requirement [2].

Thermodynamics of Modularity: Structural Costs Beyond the Landauer Bound

Complex computations typically occur via the composition of modular units, such as the universal logic gates found in logical circuits. The benefit of modular information processing, in contrast to globally integrated information processing, is that complex global information processing is more easily and flexibly implemented via a series of simpler, localized information processing operations that only control and change local degrees of freedom.

Tracking time is less complex with quantum technologies

Complex stochastic processes describe a rich and diverse range of phenomena: natural systems such as weather, geophysical, and biological processes; and societal constructs such as financial markets and traffic networks. The essential nature of these systems necessitates our ability to track and forecast their behaviour, a task for which we generally turn to large-scale simulations. Such simulations are resource intensive, requiring extreme amounts of memory, limiting the precision to which these complex systems can be studied.

Graph grammars that generate networks of chemical reactions

According to the classical definition, the chemical kinetics is a discipline that studies molecular compounds and the changes they undergo when reacting with each other. In the most complex cases, it is convenient to represent reaction kinetics with a reaction network -- a bipartite graph in which nodes represent reactions and species, whereas directed edges represent the participation of a species in respective reactions. A reaction network can be processed to obtain a system of non-linear differential equations, the so called zero-dimensional model.

Universality of non-normality in real complex networks

Network theory has been a groundbreaking research field in science for the last 20 years, conceivably the only one that could glue together disparate and even contrasting disciplines such as physics, economy, biology or sociology. A network materialises the complex interactions between the composing entities of large systems, it thus defines the natural and structural backbone for describing complex systems, which dynamics is unavoidably bound to the network properties.

Can visibility network degree detect abrupt transitions in time series?

How to identify an upcoming transition in a time series from different dynamical systems continues to be an open research issue. In various fields of physical science such as environment, economics, neuroscience and engineering, abrupt transitions can occur unexpectedly and are difficult to manage during the temporal evolution of the dynamic system. In this study we address the problem using the degree centrality measure from the complex network analysis of time series.


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Conference Organiser: NBEvents

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Photo of Thessaloniki seafront courtesy of Juli Bellou
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