[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

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The project emerges from his general program on the Mathematical Physics and Predictability of Complex Coevolutionary Systems.

For a start, the record is made straight on coevolution. The current status of the art is dominated by dynamical system formulations that essentially revolve on kinematic geometry – where physics are notoriously absent. However, unlike popular belief, coevolution is not about a history of interaction between variables over time, i.e. it is not about kinematic geometry. Dynamic coupling, scale interactions and feedbacks are important in a dynamical system, but none of them entail coevolution.

Framing coevolution in the light of theoretical physics enables us to elicit not only its governing principles but also the associated thermodynamic optimality. This in turn is fundamental to lift the theory from an optimality-bound fate to a more realistic setting in which optimality itself is challenged. By eliciting underlying mechanisms governing such challenges, we unveil the fundamental laws governing the breach of optimality principles, generalising thermodynamic theory with a new overarching principle.

The links to the classical theories will be done by showing that all four principles of thermodynamics are in essence particular cases of our general principle.

As happens with all our purely scientific projects, this initiative is independent from any commercial, political or academic constraints. Our ultimate quest is the fundamental quest for knowledge.

Even so, the project provides a significant edge to design novel energy solutions, which we develop for trade through Meteoceanics Energy.

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*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*.

Other initiatives can be formulated and scheduled on request.

]]>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.

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