Hungarian Association for Innovation
Model space reduction of deterministic systems with nonlinear time evolution
One of today’s greatest scientific challenges is the task of understanding and modeling real-world, complex systems. The word complex stands for the fact that the behavior of such systems originates from various interactions between a huge number of elements; e.g. properties of mesoscopic materials, financial events, evolution of social groups both on local and global scales, and their links to possible future changes in the Earth’s climate and biodiversity. Although several types of complex systems can be distinguished depending on the strength and quality of the interaction between the elements, due to their complexity, answering most of the questions arising from the desire of possessing models describing them with practical applicability remains an outstanding trial of computational and intellectual strength. In my research, I investigated the question of whether and how a model describing a complex system with a large number of parameters can be reduced to one with a smaller number of elements, decreasing the time and computer storage required to find out important features about the system. Due to the very general formulation of the problem, these results may have applications in the optimization and deeper understanding of robust nonlinear models which I want to investigate in the future.