Special Session #19

Foundations of Complexity Science and Applications to Engineering and Brain Dynamics

 

Chair:

Prof. Tassos Bountis, University of Patras, Department of Mathematics, Greece; e-mail: tassosbountis@gmail.com

Co- Chairs:

Prof. Anastasios Bezerianos, University of Patras, Department of Medicine, Greece; e-mail: anastasios.bezerianos@gmail.com

Assoc. Prof. Efthymia Meletlidou, Aristotle University of Thessaloniki, Department of Physics, Greece; e-mail: efthymia@auth.gr


Some 50 years after at the turn of the 20th century, when Henri Poincaré revealed the unpredictability of solutions to the 3-body problem of celestial mechanics, the new field of nonlinear dynamics was born. This was mainly due to the Kolmogorov-Arnold-Moser (KAM) theory, which established that the opposite is also true, i.e. that many body systems can also exhibit generically stable and predictable behavior. Subsequently, in the 1960’s and 1970’s, chaos was added as a principal component of the dynamics of nonlinear systems. Afterwards, in the 1980’s and 1990’s, another new field called complexity was born to tackle multi-dimensional systems in all the fields of sciences. In 2021, the importance of this field was recognized by the Nobel Physics prize, awarded for contributing to the understanding of the evolution of the Earth’s climate. Currently, there is practically no scientific field where complexity has not yet made remarkable advances, from the natural sciences to engineering, computation, medicine and social sciences.

In this special session, we aim first to elucidate, through important examples, some of the main contributions of complexity to the physical, medical and engineering sciences, by focusing on some of its major physical and bio-engineering applications.

The topics include but are not limited to:

  • Nonlinear dynamics, global stability and chaos theory
  • Celestial mechanics of planetary systems
  • Nonlinear wave equations, solitons and integrability
  • Bio-medical and bio-engineering applications
  • Time series analysis and prediction methods
  • Applications of signal and image processing techniques
  • Complex networks and applications to biology
  • Plasma physics and nonlinear optics