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Sparsity is an important paradigm for extracting useful information from indirectly measured data, and constructing easily interpretable parsimonious models  in the context of ill-posed inverse problems in heterogeneous domains.

Topics include:
1. mathematical theory (e.g. dual formulation, convergence analysis, parameter choice, source conditions, approximate sparsity);
2. statistical and Bayesian formulations of sparsity constrained inverse problems;
3. algorithms for practical multiscale inverse problems and novel applications (e.g. pedestrian flows in urban environments, chemical separation processes, resouces allocation in big data, image processing);
4. identification of parameters, model structures and fluctuations in deterministic and stochastic homogenization.

Tutorial lectures will be given for junior scientists and newcomers to the field.

When? 22-26 August, 2016
Where? Loka Brunn

Please, submit your title and abstract to the organizers asap before the end of June.

The conference is organized by ¹û¶³´«Ã½ and Örebro University.