Morphogenesis in Robotic Swarms

New results about bio-inspired technics applied in robotics, published in Science Robotics.

Inferring causation from time series in Earth system sciences

A perspective article in Nature Communications on causality inference techniques specifically for climate sciences

Thesis defence Vivek Sheraton Muniraj

On Wednesday 19 June Vivek Sheraton Muniraj will defend his thesis "The Emergence of Biofilms: Computational and Experimental Studies" Time: 12:00 Location: Agnietenkapel, Amsterdam

Peter Coveney, professor by special appointment of Applied High Performance Computing

Coveney will research codes which run efficiently on the largest petascale and exascale architectures which are expected to emerge in the near future. He also aims at building complex problem-solving environments.

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.

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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.”

Upcoming events

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Twitter Feed (@UvA_CSL)

Great work by our lab's very own Jaap Kaandorp!

Indeed, let's 'causify' the field! 🙂 And a big thank you to @jakob_runge and others, was a joy! https://t.co/gIJsBbN4OB

Peter Sloot @Peter_Sloot and Valeria Krzhizhanovskaya have opened the 19th ICCS @iccs_conf

Our new paper "Risk Factor Evolution for Counterparty Credit Risk under a Hidden Markov Model" with Drona Kandhai is out in @Risks_MDPI journal @BigDataITN @UvA_CSL @UvA_IAS @Mariecurie_alum #mdpirisks #HMM #CRR #BigDataFinance https://t.co/SdfmjQhiDL

Daan Crommelin gave an introduction to Uncertainty in Simulation science last Friday at @UvA_IAS for #SbSclub and @UvA_CSL

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