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: mdsc@meteoceanics.eu.

KEY TOPICS

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.

Duration: 50 hours + self-practice.

Editions: A) Academic; B) Business.

Chair: Prof. Dr. Rui A. P. Perdigão.

Cite as:
Perdigão, R.A.P. (2021): Course on Nonlinear Frontiers: From Dynamical Systems, Information and Complexity to Cutting-Edge Physically Cognitive Artificial Intelligence. https://doi.org/10.46337/uc.210211.

Next Courses: Fall Semester 2022