Physical Information among Statistically Independent Processes in Coevolutionary Systems

There is a common misconcepton that statistically independent processes cannot share any information. In reality, the absence of statistical codependence does not preclude the existence of physical interactions. In order to bring out and characterize that hidden information in terms of microphysical interaction indicators, Rui A. P. Perdigão has formulated a new set of informaton measures and introduced them in [1]. The findings stress the relevance of taking nonlinear microphysical coevolution into account when formulating information measures, especially when a system is undergoing mixing among subsystems such as in thermodynamic coevolutionary settings.

[1] Perdigão, Rui A.P. (2018): Polyadic Entropy, Synergy and Redundancy among Statistically Independent Processes in Nonlinear Statistical Physics with Microphysical Codependence. Entropy 2018, 20(1), 26; doi:10.3390/e20010026

 

Listings in Meteoceanics Education

Within the chair in Fluid Dynamical Systems and the Meteoceanics Interdisciplinary Centre for Complex System Sciences, the following disciplines are nurtured and delivered by our coordinator Prof. Dr. Rui Perdigão:

Semester-long Disciplines:

  • Fluid Dynamical Systems: introductory (BSc/MSc) and advanced (MSc/PhD);
  • Information Physics and Evolutionary Complexity: advanced (MSc/PhD);
  • Earth System Dynamics: introductory (BSc/MSc) and advanced (MSc/PhD);
  • Dynamic Meteorology, Oceanography and Climatology: advanced (MSc/PhD);
  • Frontiers in Fluid Dynamical Systems: advanced (PhD);
  • Complex System Dynamics, Analytics and Predictability in Earth and Environmental Sciences: advanced (PhD).

Modular Disciplines:

  • Dynamical System Analytics in Physics and Engineering: introductory (MSc);
  • Non-Equilibrium Thermodynamics and Statistical Physics: advanced (PhD);
  • Mathematical Physics and Modelling: introductory (BSc/MSc) and advanced (PhD);
  • Mathematical Methods in the Geophysical Sciences: advanced (MSc/PhD);
  • Hydro-Climate Dynamics: introductory (BSc/MSc) and advanced (MSc/PhD);
  • Electrodynamics and Magnetohydrodynamics of the Earth: advanced (PhD);
  • Nonlinear Analytics and Machine Learning: advanced (PhD);
  • Information Theory and Kinematic Geometry: introductory (MSc);
  • Atmospheric Physics A: Thermodynamics: (MSc) introductory;
  • Atmospheric Physics B: Fluid Dynamics: (MSc) introductory;
  • Atmospheric Physics C: Electricity and Optics: (MSc) introductory;
  • Atmospheric Physics D: Numerical Modelling: (MSc) advanced;
  • Atmospheric Physics E: Nonlinear Geostatistics: (MSc) advanced;
  • Computational Hydrodynamics in GPU Architectures: advanced (PhD).

We further deliver the following topics both as courses and workshops:

  • Fluid Dynamical Systems
  • Mathematical Physics and Modelling
  • Information Physics
  • Nonlinear Thermodynamics, Complexity and Predictability
  • Nonlinear Analytics and Machine Learning
  • Computational Fluid Dynamics
  • Dynamic Meteorology and Oceanography
  • Hydro-Climate Dynamics
  • Earth System Dynamics.

All course materials, including manuals, software and experimental setups are available via M-DSC under copyright of the author (® Rui A. P. Perdigão).

Other initiatives can be formulated and scheduled on request.

Mathematical Physics and Predictability of Complex Coevolutionary Systems

Meteoceanics chair Prof. Dr. Rui Perdigão has launched an international program on the mathematical physics of complexity. The program entails the theoretical establishment of the fundamental principles and general governing laws for complex systems, including the far from equilibrium coevolutionary dynamics where the ergodic assumptions of classical dynamical system theories do not work. The theory provides fundamental physical understanding and dynamic predictability in non-standard systems where there are no established invariants of motion, no attractors, no definite phase space, and no scale separability (i.e. where the classical scale interaction paradigms are not possible, and the predictability of both deterministic and stochastic formulations collapse).

A series of projects are set within this program, from fundamental mathematical physics to applications including complex system modelling, big data analytics, robust assessment of environmental conditions in fast-changing theatres of operation, dynamic risk assessment and decision support under trans-critical climate change.