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Foundations of Computational Science


Foundations of Computational Science

Addressing the Challenge of Continuous Systems Modeling versus Discrete Computation theory

NetworkNature has a very efficient way to process information. Processes studied by natural scientists involve systems that are either continuous, stochastic, spatially extended, or any combination of these, and fall strictly outside the range of discrete computation theory. The study of information processing in complex dynamical multiscale systems is, therefore, still in its infancy. The basic questions to be answered are: 'Can we detect and describe the computational structure in natural processes and can we provide a quantitative characterization of essential aspects of this structure?' This simple question leads to a plethora of theoretical challenges, related to information processing, automata theory, information theory and complexity, synchronous vs. asynchronous vs. evolutionary computing, etc..

Under the strong assumption that e.g. physical and biological processes are nothing but examples of universal computation, we aim to study such questions in the context of dynamic complex systems modeled as cellular automata, individual based models, and complex networks (for details, follow the links below).




Theme Cellular Automata
Cellular Automata have the potential to perform complex computations with a high degree of efficiency and robustness, as well as to model the behavior of complex systems from nature.
Theme Complex Networks
Theory and methods of dynamical networks providing us with new insights into the underlying processes of nature, economy, and society.
Theme Individual-based Modeling
Individual-based Modeling
Theme Information Theory
Within the context of Cellular Automata as universal parallel computing machines and as generic models of dynamic complex systems, we aim to study fundamental questions related to the (distributed) computational structure of natural processes.
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