At the frontiers of contemporary science, many if not all of the quantitative research and engineering challenges with high socioeconomic impact – such as climate, energy, materials, health and disease, urbanization, economy, psychology, or sociology – are essentially multiscale system problems. Progress in most of these societal grand challenges is determined by our ability to design and implement multiscale models and simulations of the particular systems under study.
Generic methods and efficient algorithms
This project will develop generic methods and efficient algorithms for sensitivity analysis and uncertainty quantification for multiscale modelling & simulation, to implement these algorithms as high quality modules of the publically available Multiscale Modelling and Simulation Framework, and to test, validate and apply the methods on a sufficiently large portfolio of multiscale applications.
Supporting the whole range of computing infrastructure
The framework must support the whole range of computing infrastructure, from the desktop, via cluster and clouds, to high-end HPC machines. The research and development will be executed in close collaboration with the recently started FET-HPC ComPat project. With some exceptions, sensitivity analysis and uncertainty quantification for multiscale modelling and simulation is currently almost lacking but very much needed.
This project will have a significant impact by filling this gap and studying in detail the behaviour of error propagation in coupled single scale models, taking into account the different kinds of scale bridging that can be identified, and then applying that knowledge for sensitivity analysis and uncertainty quantification.
Available to the scientific community
The impact will be amplified by making these new developments available to the scientific community as high quality and computationally very efficient modules in the Multiscale Modelling and Simulation Framework.
June 11, 2018