|Link to Mercator Fellow - 1st funding phase (2014 - 2018)|
Via Giuseppe Buffi 13
+41 58 666 4123
A central scientific issue in the work programme of CRC1114 recurring in several of the individual projects is the unbiased characterization of observation, measurement, and simulation data in multiscale systems. The size of the data emerging in the CRC’s application domains, e.g., in molecular dynamics and the geosciences, together with the multiscale character of the underlying systems necessitate not only the deployment of High-Performance-ready data analysis tools, but also call for overcoming the usual parametric assumptions of common statistical tools, such as stationarity, Gaussianity or linearity.
The Mercator Fellow will offer his expertise and experience with multiscale data analysis problems in the natural sciences accumulated by him and his group at USI Lugano; he will contribute with a direct possibility to use Europe’s most powerful supercomputer “Piz Daint” (place 3 in the TOP 500) at CSCS Lugano for data analysis problems in CRC1114; and he will provide access to and user advice for the high-performance multiscale data analysis methods developed in recent years under his guidance. The latter include a Finite Element Framework for nonparametric denoising and nonstationary model inference, that are scalable to be used even on emerging supercomputing architectures involving Graphical Processing Units. The role of the Mercator Fellow in the CRC is thus twofold: He will (i) support the individual projects in addressing questions related to the analysis of multiscale data, and (ii) develop new multiscale data analysis tools and software applications, motivated by the problems and questions emerging in CRC.
Project publications (Mercator Fellow)
Gerber, S. and Olsson, S. and Noé, F. and Horenko, I. (2018) A scalable approach to the computation of invariant measures for high-dimensional Markovian systems. Sci. Rep., 8 (1796). ISSN 2045-2322
Pospisil, L. and Gagliardini, P. and Sawyer, W. and Horenko, I. (2017) On a scalable nonparametric denoising of time series signals. Communications in Applied Mathematics and Computational Science . pp. 1-28. (In Press)
Gerber, S. and Horenko, I. (2017) Toward a direct and scalable identification of reduced models for categorical processes. Proceedings of the National Academy of Sciences, 114 (19). pp. 4863-4868.
O'Kane, T.J. and Monselesan, D.P. and Risbey, J.S. and Horenko, I. and Franzke, Ch.L.E. (2017) On memory, dimension, and atmospheric teleconnection patterns. Math. Clim. Weather Forecast, 3 (1). pp. 1-27.
Horenko, I. and Gerber, S. and O'Kane, T.J. and Risbey, J.S. and Monselesan, D.P. (2017) On inference and validation of causality relations in climate teleconnections. In: Nonlinear and Stochastic Climate Dynamics. Cambridge University Press, pp. 184-208. ISBN 9781107118140
Horenko, I. and Gerber, S. (2015) Improving clustering by imposing network information. Science Advances, 1 (7). ISSN 2375-2548
O’Kane, T.J. and Risbey, J.S. and Monselesan, D.P. and Horenko, I. and Franzke, Ch.L.E. (2015) On the dynamics of persistent states and their secular trends in the waveguides of the Southern Hemisphere troposphere. Climate Dynamics . ISSN Print: 0930-7575, Online: 1432-0894