UNIVERSITY OF JYVÄSKYLÄ Modeling and Simulation visions towards 2020 Timo Tiihonen 2010

UNIVERSITY OF JYVÄSKYLÄ 2010 Ingredients of the vision  Trends in research environment  Idea of a research vision  Own expertise  Recognized contributors and stakeholders  First steps on the road map

UNIVERSITY OF JYVÄSKYLÄ 2010 Trends in the environment  Computation –Computational capacity will be increasingly distributed and heterogeneous –Multi core architectures, grid/cloud computing in multitasking environment –High capacity will be available for non specialists  Modeling –Multi disciplinary/multi-scale applications will come to main stream (to be used by non specialists) –Lot of (isolated) single model tools/solutions will be available –Users can not be assumed to be specialists in model selection and coupling

UNIVERSITY OF JYVÄSKYLÄ 2010 Research goal  How to cope with complex, multi disciplinary, multi scale computational models –How to (automatically) divide complex systems to interacting subsystems –How to select most appropriate modeling paradigm for each subsystem and assess its accuracy and complexity –How to map subsystems and tasks automatically to existing heterogeneous resources –How to encapsulate methods for sub-models to enable interaction and resource planning –How to maintain variety of methods optimized for different environments

UNIVERSITY OF JYVÄSKYLÄ 2010 Needed assets  Modeling and mathematics –Domain decomposition (art/science of coupling sub-models together so that overall model is accurate and numerical methods converge) –A posteriori error analysis (tool to assess the accuracy of local sub-models)  Software engineering –Reformulation of existing software to coordinated abilities to solve sub-models (model as a service) –Autonomous coordination of solution of sub-models using ad hoc heterogeneous resources

UNIVERSITY OF JYVÄSKYLÄ 2010 Personal position  Background –Basis in mathematical modeling –Need to shift focus towards IT on longer term (to be compatible with the faculty) –Awareness of autonomic computing (agents etc)  Possible contribution –Models with different accuracy and complexity –Use of model hierarchies during solution phase (preconditioning) –Coupling of sub-models over interfaces –Sensitivity of results w.r.t geometry of (sub)systems

UNIVERSITY OF JYVÄSKYLÄ 2010 Possible contributors  Vagan Terziyan –Model/method as a service, automated coordination of subtasks  Jaques Periaux (FiDiPro) –Scientific Cloud Computing (JYU, INRIA/Grenoble etc), database (of models and methods), multiscience  Sergiy Repin –Reliable computing (a posteriori analysis and adaptive model selection)  Raino Mäkinen –Fluid-Structure and sensitivity analysis w.r.t model coupling, automatic differentiation

UNIVERSITY OF JYVÄSKYLÄ 2010 Possible collaborators II  Tuomo Rossi –Solvers, domain decomposition, parallel computing, processor architectures  Kaisa Miettinen, Ferrante Neri –(Multi-criteria) optimization as a service, optimization and hierarchical models, metamodels  Numerola –Multi disciplinary problems, model coupling, software tools, modeling languages

UNIVERSITY OF JYVÄSKYLÄ 2010 First (feasible) steps  Toy problems for a posteriori analysis with model hierarchies (like non-linear vs linearized model)  Coupling of continuum and mesoscale models (Lattice Bolzmann and Navier-Stokes)  Language to describe modeling and solution variants (vehicle routing)  Fast PDE-solvers for GPU clusters