Events

MIT Distinguished Seminar Series in Computational Science and Engineering

Thursday, April 28th | 12:00 PM* | 37-212

Impact of horizontal resolution (1/12 to 1/50 degree) on Gulf Stream separation and penetration in a series of North Atlantic HYCOM numerical simulations
Eric Chassignet
Director, Center for Ocean-Atmospheric Prediction Studies Professor of Oceanography, Department of Earth, Ocean and Atmospheric Science
Florida State University

The impact of horizontal resolution (1/12 to 1/50 degree) on Gulf Stream separation and penetration is analyzed in a series of identical North Atlantic HYCOM configurations. The specific questions that will be addressed are as follows: When does a solution converge or is "good enough"? Are the mesoscale and sub-mesoscale eddy activity representative of interior quasigeostrophic (QG) or surface quasigeostrophic (SQG) turbulence? How well do the simulations compare to observations? We will show that the increase in resolution (1/50 degree) does lead to a substantial improvement in the Gulf Stream representation (surface and interior) when compared to observations and the results will be discussed in terms of ageostrophic contributions and power spectra.

*Lunch provided at 11:45 AM.

MIT Distinguished Seminar Series in Computational Science and Engineering

Thursday, May 12th | 12:00 PM* | 37-212

Kernel Approximations for Surrogate Modelling in Simulation Science
Bernard Haasdonk
Professor for Numerical Mathematics, University of Stuttgart
Institute of Applied Analysis and Numerical Simulation

Data-based approaches are gaining increasing attention for generating or improving simulation models in CSE. Application settings comprise modelling from data, i.e. measurements are given, and we aim to find a model, that can be used for simulation, or approximative surrogate modelling, where a model is given and a cheap surrogate model is constructed based on simulation data of the former.

In this presentation I focus on kernel methods for generating such models. These powerful techniques have proven to be successful in various applications in data-science such as pattern recognition, machine learning, bioinformatics, etc. In addition to relevant applicability, they also enable elegant mathematical analysis in so called reproducing kernel Hilbert spaces (RKHS).

In the context of simulation models, kernel methods can be used for sparse vectorial function approximation, for example by vectorial support vector regression or the vectorial kernel orthogonal greedy algorithm (VKOGA). For the VKOGA theoretical analysis can be given in terms of local optimality and convergence rates [2]. The resulting approximants allow efficient complexity reduction in projection-based model order reduction [1] or in multiscale problems as demonstrated on applications from biomechanics and porous media flow [3].

References:

[1] Wirtz, D. & Haasdonk, B.: Efficient a-posteriori error estimation for
nonlinear kernel-based reduced systems, Systems and Control Letters,
2012, 61, 203 - 211.

[2] Wirtz, D. & Haasdonk, B.: An Improved Vectorial Kernel Orthogonal Greedy Algorithm,
Dolomites Research Notes on Approximation, 2013, 6, 83-100.

[3] Wirtz, D.; Karajan, N. & Haasdonk, B.: Surrogate Modelling of multiscale models using
kernel methods, International Journal of Numerical Methods in Engineering,
2015, 101, 1-28.

*Lunch provided at 11:45 AM.