Foundations of Computational Science
Foundations of Computational Science
Addressing the Challenge of Continuous Systems Modeling versus Discrete Computation theory
Nature 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).
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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.
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Complex Networks
- Theory and methods of dynamical networks providing us with new insights into the underlying processes of nature, economy, and society.
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Individual-based Modeling
- Individual-based Modeling
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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.
NWO VIDI for Marten Postma
