Research themes in the Computational Science research group

Themes

Our research is organized around the following themes:

• Foundations of Computational Science
• Applications of Computational Science
  
• Problem Solving Environments for Computational Science
  

Description of the field

Recent advances in experimental techniques such as detectors, on-line sensor networks and high resolution (medical) scanners, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion requires sophisticated distributed computing environments as well as collaborative virtual laboratories, in order to register, transport, store, manipulate and share the data. The complete cascade from the individual components to the integrated multi-science systems crosses many orders of magnitude in temporal and spatial scales. These complex systems display endless signatures of order, disorder, self-organization and self-annihilation. Understanding, quantifying and handling this complexity is one of the biggest scientific challenges of our time.

Research questions

The basic problem is that 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 such 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, etcetera. Within the Computational Science research program we aim to study such questions through numerical simulations with cellular automata, complex networks and individual based models. In summary, we address fundamental questions related to the (distributed) computational structure of natural processes.

Challenges

The sheer complexity and range of spatial and temporal scales defies any existing numerical modelling and computational capacity. The only way out is by combining data on all levels of detail with large scale particle-based, stochastic and continuous models; an open research area. The challenges include modeling multi-level systems and their dynamics and integrating numerical simulations with massive sets of heterogeneous and often incomplete data in virtual laboratories. Conceptual, theoretical and methodological foundations are necessary in understanding these multi-scale processes, dynamic networks, and the associated predictability limits of such large-scale computer simulations.

Collaboration

We have established a number of long lasting strategic collaborations in fields where modeling and simulation is currently emerging (infectious diseases, biomedical applications, and systems biology) and intend to focus our research collaborations in those application fields. The core of the research for the next five years combines this application driven modeling and simulation with research on problem solving environments and advanced scientific visualization. Moreover, we will further strengthen collaboration with ‘data producers’ and/or ‘data owners’. All our research will be data-driven, even to the point that we start collecting data ourselves (e.g. harvesting from on-line resources).