Uncertainty quantification of a three-dimensional in-stent restenosis model with surrogate modelling

Dongwei Ye et al. have published a new paper on the uncertainty quantification of a 3D in-stent restenosis model in the Journal of the Royal Society Interface.

Best Application Paper Award at the EuroXR2021 conference

for their paper “A prototype for the treatment of children with selective mutism using interactive 360° VR”

The Effects of Micro-vessel Curvature Induced Elongational Flows on Platelet Adhesion

Christian Spieker et al. have published a new paper in the Virtual Physiological Human special issue of the Annals of Biomedical Engineering.

Effects of local coronary blood flow dynamics on the predictions of a model of in-stent restenosis

Pavel Zun, Andrey Svitenkov and Alfons Hoekstra have published a new paper in Journal of Biomechanics.

Non-intrusive and semi-intrusive uncertainty quantification of a multiscale in-stent restenosis model

D. Ye et al. published a new paper in Reliability Engineering & System Safety

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.”

Twitter Feed (@UvA_CSL)

And Yue also presenting her work on image based flow in platelet aggregates. @UvA_CSL

Dongwei presenting his work on ROM and surface registration at SIAM CSE2023

Simulating the multicausality of Alzheimer’s disease with system dynamics. Great work by Jeroen Uleman, great collaboration with Marcel Olde Rikkert and his team.



Great paper by Claire Miller and the INSIST team on Insilico
thrombectomy trials for acute ischemic stroke.


@UvA_CSL @Lab42UvA @InSilicoWorld @INSIST_H2020

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