Earth System Dynamic Intelligence with Quantum Technologies: Seeing the “Invisible”, Predicting the “Unpredictable” in a Critically Changing World
We hereby embark on a frontier journey articulating two of our flagship programs – “Earth System Dynamic Intelligence” (Perdigão 2021) and “Quantum Information Technologies in the Earth Sciences” (Perdigão 2020) – to take the pulse of our planet and discern its manifold complexity in a critically changing world. Going beyond the traditional stochastic-dynamic, information-theoretic, artificial intelligence, mechanistic and hybrid approaches to information and complexity, the underlying fundamental science ignites disruptive developments empowering complex problem solving across frontier natural, social and technical geosciences. Taking aim at complex multiscale planetary problems, the roles of our flagships are put into evidence in different contexts, ranging from I) Interdisciplinary analytics, model design and dynamic prediction of hydro-climatic and broader geophysical criticalities and extremes across multiple spatiotemporal scales; to II) Sensing the pulse of our planet and detecting early warning signs of geophysical phenomena from Space with our Meteoceanics QITES Constellation, at the interface between our latest developments in non-linear dynamics and emerging quantum technologies.
Perdigão, R.A.P. (2021): Earth System Dynamic Intelligence with Quantum Technologies: Seeing the “Invisible”, Predicting the “Unpredictable” in a Critically Changing World. https://doi.org/10.46337/211028.
From frontier research and education to cutting-edge development empowering scientific excellence, literacy and wisdom.
We have launched the North Atlantic Climate Centre as a new international system-of-systems allying world-class partner institutions into a coherent, solid, reliable partner to all those seeking to understand, nurture and protect the society and the environment.
The North Atlantic Climate Centre encompasses Climate in its broadest sense, across an interdisciplinary interface among frontier natural, social and technical sciences, whilst preserving an extraordinarily strong scientific and methodological core in climate system science, grounded on world-class mathematical physics, geophysical sciences and information technologies, classical and quantum. Synergies with our QITES Alliance and NORA flagships are under way.
Our international academic, scientific and operational projects are already contracted and unfolding since day one in a self-sustainable manner and generating value with frontier science and cutting-edge innovation to solve real problems.
As for the Centre online presence, it is currently at an early stage, yet already worth a visit:
In our latest Science publication, the Perspective “Who is stirring the Waters?” by Julia Hall and Rui A. P. Perdigão, we offer new emerging pathways to strengthen the science of causal attribution, of relevance to better understand our coevolutionary world.
“Information physics can pave the way for bringing physical meaning to inferential metrics, and a dynamic coevolving flexibility to the statistical metrics of information transfer, bringing new pathways for causal discovery and attribution.”
From Dynamical Systems, Information and Complexity to Cutting-Edge Physically Cognitive Artificial Intelligence
Analysing and modelling complex systems often reach technical and theoretical limits under state-of-the-art statistical and computational methods. Their underlying assumptions often encompass structural-functional symmetries and recurrence. However, until very recently the dynamics and predictability of far-from-equilibrium non-ergodic entanglement and coevolution remained elusive.
Our recent advances, ranging from theoretical physics to information theory and cognition, have overcome these issues. This program disseminates our novel theories and applications to empower cutting-edge analysis, modelling and decision support pertaining complex real-world problems.
A set of cutting-edge methodologies is laid out for rigorous analysis and modelling complex dynamic systems, associated predictability and uncertainty, along with the underlying theoretical background and enabling technologies. These enable the robust retrieval and investigation of fundamental dynamic mechanisms and interactions, extending predictability limits and empowering new choices for improving decision support pathways.
The program will guide participants along an excursion through nonlinear frontiers in complex system science, ranging from fundamental physics to artificial intelligence and new cutting-edge developments. We further invite participants to bring their own data, problems and application questions to explore hands-on implementation of the concepts and tools to their fields of interest.
Scholarship support is available to co-sponsor top–tier candidates. For further information, queries and quotes contact: email@example.com.
Module 1: From Dynamical Systems to Information Theory & Complexity
Fundamentals from dynamical systems, information theory, thermodynamics and complexity.
Module 2: Information Physics and Coevolutionary Dynamical Dystems
When invariants of motion are no longer so: mathematical physics of complex coevolutionary systems.
Module 3: Information Retrieval and Model Design in Complex Systems
From deep machine learning and artificial intelligence to information theoretical evolutionary cognition.
Module 4: Reconciling Artificial Intelligence with Fundamental Physics
New frontiers in mathematical and information physics for realistic cognition, discovery and design.
Module 5: Interdisciplinary Solutions across nature, society & technology
From Earth system dynamics and extremes to socio-environmental modelling and decision support.
Dynamic System Analytics in the Earth System Sciences
Analysing complex and dynamic earth and environmental systems often reaches technical and theoretical limits of statistical standard tools. This is the case as most classical statistical approaches fundamentally require assumptions about statistical independence and stationarity, which are especially problematic as soon as we deal with non-linearity and coevolution. Many promising approaches founded in scientific fields ranging from theoretical physics to information theory have been developed. Still, their application to applied climate and environmental sciences remains challenging.
This 6 ECTS course introduces a set of modern tools for rigorous analyses of dynamical systems. Starting off with examples from geophysics and fluid mechanics the course will guide you during an excursion through stochastic physics, information theory, phase-state analyses and applications with big data and scaling. We especially invite you to bring own data and application questions to provide hands-on utilisation of the concepts and tools within your field of research.
1 – From fluid mechanics to dynamical systems
The first module provides a common basis for all participants. With fundamentals from classical fluid dynamics, thermodynamics, stability and scaling laws the foundation is laid. The theoretical lectures are complemented with practical analytical and numerical examples across the earth sciences.
2 – Coevolutionary dynamical systems
The second module extends the classical fluid dynamics with stochastic physics and information theory. With this, complexity is rigorously treated in a simple and coherent framework providing the physical background to coevolutionary dynamics and organisation. The module is halved into theoretical derivations and real-world applications from multiscale geophysical fluid dynamics.
3 – Mastering conjugated dimensions
The third module will redefine the state-space system analyses with physical principles and thermodynamic limits as enhanced phase-state analyses. This steers the participants towards a thorough dynamic system understanding without the requirement for single attractors, finite phases and fixed scales. It also includes conjugated pairs and a new breed of non-paired conjugates. The theoretical derivations are followed by analytical and numerical exercises from simple synthetic cases to real-world applications.
4 – Implementation and frontier topics
The fourth module will start with the application of the prior modules to systems chosen by the participants, who are encouraged to bring associated resources (e.g. datasets). Subsequently frontier topics of coevolutionary scaling and big data applications will be motivated, by presenting ongoing methodological development projects where participants are encouraged to engage.
Professor Rui Perdigão’s “Interdisciplinary Data Analytics and Model Design”, anchored on the Meteoceanics Institute for Complex System Science, is now also available as a semester doctoral course at the Interuniversity Institute for Intelligence, Complexity and Predictability, offering 6 ECTS for students enrolled in programs from partner universities.
Acquisition of fundamental competences in data analysis, its relevance and implementation in the conceptualization and formal analysis of systems in an interdisciplinary perspective;
Learning fundamental techniques for information retrieval, analysis and treatment along with its uncertainties, from data acquisition to model design;
Acquisition of new competences in scientific research, development and communication at the interface between natural and social sciences;
Special emphasis on interdisciplinary challenges of climate change and decision support towards sustainable development.
Beneath Data, there is a Story: Fundamental principles behind the nature, geometry and dynamics of information across natural, social and joint systems;
Retrieving the Story: Fundamental methods for data analytics and model design. From spatiotemporal geostatistics to broader dynamic information tools for data mining, pattern recognition, causal analysis and model design;
Quality-checking the Story: Techniques for quality check, uncertainty assessment and data processing towards strengthening information reliability;
Sharing the Story: Techniques for data visualization, information sharing and overall communication of scientific results;
GeoSys Operation: Operational real-world examples for a) data mining and machine learning in large satellite datasets; b) nonlinear analytics and model design for earth system dynamics; c) early warning and automated decision support systems in natural (e.g. hydro-meteorological, geophysical) hazards;
Frontier Operation: early warning detection and adaptive decision support of critical transitions and extremes in the earth system under climate change;
Hands-On: Simple analytical and computational examples on the prior points.
Professor Rui Perdigão’s Complex System Dynamics, anchored at the Meteoceanics Institute for Complex System Science, is now also available as a semester doctoral course at the Interuniversity Institute for Intelligence, Complexity and Security, offering 6 ECTS for students enrolled in programs from partner universities.
Acquisition of fundamental competences in complexity sciences, their relevance and implementation in the conceptualization, systematization, modeling and formal analysis of the complex dynamics underlying climate change;
Learning fundamental principles that allow to formulate the dynamics of complex systems, including emergence of extreme phenomena, in an elegantly simple and effective way without loss of rigor nor generality;
Deepening scientific research, development and communication at the interface between natural and social frontier sciences.
Fundamental notions on the dynamics of complex systems, principles and underlying mechanisms in dynamic systems theory and physical information;
Methods of systematization of dynamic systems: simple conceptual structures representing complex natural, technical and social phenomena;
Fundamentals of the dynamics of the Earth system and the emergence of regimes, critical transitions and extreme events in the context of complexity sciences;
Coevolutionary models of climate change in a holistic perspective involving dynamics of the oceans, atmosphere, geosphere, biosphere and society;
Dynamic methods of extraction and analysis of information related to the dynamics of complex systems, from empirical and computational records;
Detection of patterns of spatial and temporal climatic variability from data of the dynamics of the Earth system and attribution to underlying mechanisms;
Methods of evaluating uncertainty and predictability in complex system dynamics, for representative model optimization and decision support.