The ever increasing complexity of technological systems in science and engineering implies a persistent need for accurate and aggregated models that are able to predict, monitor and represent the dynamic behavior and features of physical phenomena. Typical demands on these models vary from specifications on reliability, computability, efficiency, complexity, accuracy, scalability, convergence and processing power. Despite the unprecedented advancements in high performance computing, the multiscale dynamical nature of systems causes these demands to be largely conflicting. The field of scientific computing therefore faces serious challenges in improving the precision and accuracy of modeling and simulation tools, in managing the complexity of models, and in improving the computational efficiency of large-scale models. This symposium aims to address these challenges by presenting a number of novel developments in the field of multiscale dynamical models, in model approximation and in high performance computing.

Marcus Müller (Institut für Theoretische Physik, Georg-August-Universität, Göttingen, Germany)

George Karniadakis (Charles Pitts Robinson and John Palmer Barstow Professor of Applied Mathematics, Brown University, Providence, Rhode Island (USA); also at MIT)

Oliver Henrich, School of Physics and Astronomy, The University of Edinburgh, Schotland

Jan Dirk Jansen (Department of Geoscience & Engineering, Faculty of Civil Engineering and Geosciences, TU Delft)

Enrico Camporea (Multiscale Dynamics Group, Centrum voor Wiskunde en Informatica, Amsterdam)