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Modelling and simulation of multiscale systems constitutes a grand challenge in computational science, and is widely applied in fields ranging from the physical sciences and engineering to the life sciences and the socio-economic domain. To adequately simulate numerous intertwined processes characterized by different spatial and temporal scales (often spanning many orders of magnitude), sophisticated models and advanced computational techniques are required. Additionally, these multiscale models frequently need large scale computing capabilities as well as dedicated software and services that enable the exploitation of existing and evolving computational ecosystems. The aim of the annual Workshop on Multiscale Modelling and Simulation is to facilitate the progress in this multidisciplinary research field http://www.computationalscience.nl/MMS/ . In this paper, we reflect on the 12 years of workshop history and glimpse at the latest developments presented in 2015 in Iceland, the Land of Fire and Ice. In Section 6, we invite new workshop co-organizers. [ABSTRACT FROM AUTHOR]

Multiscale Modelling and Simulation (MMS) is a cornerstone in the today’s research in computational science. Simulations containing multiple models, with each model operating at a different temporal or spatial scale, are a challenging setting that frequently require innovative approaches in areas such as scale bridging, code deployment, error quantification, and scientific analysis. The aim of the MMS workshop is to encourage and consolidate the progress in this multidisciplinary research field, both in the areas of the scientific applications and the underlying infrastructures that enable these applications. Here we briefly introduce the scope of the workshop and highlight some of the key aspects of this this year’s submissions. [ABSTRACT FROM AUTHOR]

Statistically Similar Representative Volume Element (SSRVE) is used to simplify computational domain for microstructure representation of material in multiscale modelling. The procedure of SSRVE creation is based on optimization loop which allows to find the highest similarity between SSRVE and an original material microstructure. The objective function in this op- timization is built upon computationally intensive numerical methods, including simulations of virtual material deformation, which is very time consuming. To avoid such long lasting calcu- lations we propose to use the data farming approach to identification of SSRVE for Advanced High Strength Steels (AHSS) characterized by multiphase microstructure. The optimization method is based on a nature inspired approach which facilitates distribution and parallelization. The concept of SSRVE creation as well as the software architecture of the proposed solution is described in the paper in details. It is followed by examples of the results obtained for the iden- tification of SSRVE parameters for DP steels which are widely exploited in modern automotive industry. Possible directions for further development as well as possible industrial applications are described in the conclusions. [ABSTRACT FROM AUTHOR]