Data assimilation combines the state of a model with incoming data by solving an inverse problem to balance the uncertainty between them. Data assimilation is used in applications from computer vision and remote sensing to weather forecasting.
Data assimilation combines the state of a model with incoming data by solving an inverse problem to balance the uncertainty between them. Data assimilation is used in applications from computer vision and remote sensing to weather forecasting. Physical models are formulated as partial differential equations, which have solutions in spaces of functions.
Their discretization leads to high-dimensional problems, and uncertainty can be modeled by stochastic ensembles of simulations. The lecture will discuss the principles of data assimilation with infinitely dimensional state and data, and show the convergence of the ensemble Kalman filter with increasing ensemble size independently of the state and data dimensions, including infinite dimension.
Jan Mandel is Professor and Chair at the Department of Mathematical and Statistical Sciences, University of Colorado Denver. His early work was in areas including discrete optimization, transonic flows, variational inequalities, and aggregation in input-output economic models, which led him to multigrid and domain decomposition methods for partial differential equations. His current interests include design and mathematical analysis of data assimilation algorithms, modeling of wildfires, remote sensing, and assimilation of satellite data. His research has been funded by the National Science Foundation, NASA, and Czech Science Foundation. He published over 150 papers in journals, books, and proceedings.
Its program consists of a one-hour lecture followed by a discussion. The lecture is based on an (internationally) exceptional or remarkable achievement of the lecturer, presented in a way which is comprehensible and interesting to a broad computer science community. The lectures are in English.
The seminar is organized by the organizational committee consisting of Roman Barták (Charles University, Faculty of Mathematics and Physics), Jaroslav Hlinka (Czech Academy of Sciences, Computer Science Institute), Michal Chytil, Pavel Kordík (CTU in Prague, Faculty of Information Technologies), Michal Koucký (Charles University, Faculty of Mathematics and Physics), Jan Kybic (CTU in Prague, Faculty of Electrical Engineering), Michal Pěchouček (CTU in Prague, Faculty of Electrical Engineering), Jiří Sgall (Charles University, Faculty of Mathematics and Physics), Vojtěch Svátek (University of Economics, Faculty of Informatics and Statistics), Michal Šorel (Czech Academy of Sciences, Institute of Information Theory and Automation), Tomáš Werner (CTU in Prague, Faculty of Electrical Engineering), and Filip Železný (CTU in Prague, Faculty of Electrical Engineering)
The idea to organize this seminar emerged in discussions of the representatives of several research institutes on how to avoid the undesired fragmentation of the Czech computer science community.
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