About me:
Adrian Guel holds both a Bachelor’s and a Master’s degree in Engineering, with experience in research, design and education. Specifically, Mr Guel’s experience lies in Dynamical and Complex Systems, Control Engineering, Statistics and Programming. Mr Guel has developed: Control Algorithms, Control Implementations, System identification and Modelling, Forecasting Methods, and Optimisation Algorithms. During his PhD, he focused on the analysis of models and control for Complex Systems using stochastic methods and information theory.In physics, a complex system is a set of dynamical systems that is characterized by the presence of a large number of interconnected components that interact with each other. Complex systems may arise and evolve through self-organization (a dynamical process by which a system spontaneously forms non-trivial macroscopic structures and/or behaviours over time), such that they are neither completely regular nor random, permitting the development of emergent behaviours at macroscopic scales. Since emergent behaviours in complex systems can be interpreted as the resistance of entropic decay and dissipation, similar to controlled systems that maintain their states around set-points despite perturbations from the environment, our hypothesis is that:
We can mimic emergent behaviours principles to artificially construct organised (controlled) complex systems. Furthermore, by interpreting entropy as a level of ignorance via information-theoretic constructs, we can diagnose abrupt changes, describe causal relations, or control the level of ignorance we have on the system dynamics.
In this regard, my PhD thesis aimed to develop a set of tools to understand and control a set of complex systems through the interconnection of the fields of information geometry, stochastic thermodynamics, and control engineering. From information geometry, we establish a metric that helps us quantify variability in the given complex systems subject to randomness. From control engineering, we recover the concept of ``feedback'' to inform, in this manner, a suitable control algorithm that will organise the system dynamics. Since efficiency is usually defined thermodynamically, Stochastic thermodynamics gives us the effects of the proposed regulator on the entropy production linked to Helmholtz free energy. Additionally, information geometry and stochastic thermodynamics give us tools to detect abrupt events and correlations between the variables in stochastic dynamics.
Research groups I am a member of: