Smile and default
Gabor Zavodszky 3 months ago

Smile and default: the role of stochastic volatility and interest rates in counterparty credit risk. Read More

Efficient Estimation of Sensitivities for Counterparty Credit Risk with the Finite Difference Monte-Carlo Method
Gabor Zavodszky 3 months ago

According to Basel III, financial institutions need to charge a Credit Valuation Adjustment (CVA) to account for counterparty default risk. This adjustment is typically driven by a large number of uncertain risk factors, which makes efficient computation of CVA and the corresponding risk measures a complex mathematical and numerical modelling problem. In “Efficient Estimation of Sensitiviti… Read More

Happy Halloween from the CSL group!
Gabor Zavodszky 5 months ago

The group was working hard, and finally these are the results for this year! We wish you a happy Halloween!… Read More

Towards the virtual artery: a multiscale model for vascular physiology at the physics–chemistry–biology interface
Gabor Zavodszky 6 months ago

This discussion paper introduces the concept of the Virtual Artery as a multiscale model for arterial physiology and pathologies at the physics–chemistry–biology (PCB) interface. The cellular level is identified as the mesoscopic level, and we argue that by coupling cell-based models with other relevant models on the macro- and microscale, a versatile model of arterial health and disease can b… Read More

Scaling of shear-induced diffusion and clustering in a blood-like suspension
Brecht Schipper 11 months ago

Lampros Mountrakis et. al published a paper in Europhysics Letters where they demonstrate that shear-induced diffusion of red blood cells (in a two-dimensional model system) does not follow the established linear scaling with shear rate for high hematocrits. They hypothesise that collective effects in the suspension are key to understand this phenomenon, and by performing cluster analyses they sho… Read More


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

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.