Journal of Computational Science impact factor has grown 62%

Now it is IF=1.75!

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Revisiting the use of the immersed-boundary lattice-Boltzmann method for simulations of suspended particles

A research article by L. Mountrakis, E. Lorenz, and A. G. Hoekstra recently published in Physical Review E.

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Successful ICCS from Switzerland to China

ICCS 2017 a success in Zurich; next year in Wuxi, China.

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A novel technique to combine and analyse spatial and temporal expression datasets

The research article on “A novel technique to combine and analyse spatial and temporal expression datasets: A case study with the sea anemone Nematostella vectensis to identify potential gene interactions” by Amir M. Abdola, Eric Röttinger, Fredrik Jansson, and Jaap...
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Liquidity risk in derivatives valuation

The research article on "Liquidity risk in derivatives valuation: an improved credit proxy method" by Dr. Sumit Sourabh (CSL, UvA), Dr. Markus Hofer (ING Bank) and Prof. Drona Kandhai (ING Bank and CSl, UvA) was recently published in Quantitative Finance. The main...
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Group Mission

We live in a highly connected and strongly coupled world, and are surrounded by a large diversity of complex systems. All these systems have one thing in common: they process information. We aim to understand this information processing in such dynamic multi-level complex systems.

Can we detect and describe the computational structure in dynamic processes and can we provide a quantitative characterization of essential aspects of this structure? When modeling for instance traffic in a city, the interactions between the individuals driving the cars, the bicycles, and pedestrians result in a dynamic self-organizing interaction structure, which is superimposed on the road network. This can be seen as a dynamical computational structure where information is exchanged, stored, and processed. What are the essential aspects of this structure, and how do they determine the way in which information is actually stored, transferred, and processed in complex systems? And what does that mean for the overall system behavior, that is, for their emergent properties? Can we then better understand emergent properties and critical phenomena such as tipping points? For instance, where do traffic jams come from, which all of sudden seem to appear from thin air? Or, can we get a deeper understanding of the systemic economic crises that struck us in 2008? Are we able to steer or control such emergent properties? What can we do to prevent traffic jams while maintaining the throughput on the road? Maybe by bringing down velocity, or slightly controlling the traffic entering a road on the individual car level? Or more dramatically, can we ‘nudge’ the behavior of countries, large companies, and/or individuals to fight the climate change? The ever increasing and abundant availability of data, both from science and society, drives our research. We study complex systems in the context of methods like multi-scale cellular automata, dynamic networks and individual agent based models. The challenges include data-driven modeling of multi-level systems and their dynamics as well as conceptual, theoretical and methodological foundations that are necessary to understand these processes and the associated predictability limits of such large-scale computer simulations.

“Nature is a Complex System that processes information. Computational Science aims to make the complexity of those systems tractable.”

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