Research Group of Prof. Dr. M. Griebel
Institute for Numerical Simulation
maximize

Dr. Bastian Bohn

Address: Institut für Numerische Simulation
Wegelerstr. 6
53115 Bonn
Germany
Office: We6 6.009
Phone: +49 228 7360452
E-Mail: bohn.ins.uni-bonn.de

Research Projects

DFG SFB 1060: The Mathematics of Emergent Effects
Project C4: Multilevel sparse tensor product approximation for manifolds and for functions and operators on manifolds

Completed Research Projects

BMBF support program: Mathematik für Innovationen in Industrie und Dienstleistungen
SIMDATA-NL: Nichtlineare Charakterisierung und Analyse von FEM-Simulationsergebnissen für Autobauteile und Crash-Tests
Cooperation with AUDI AG, Fraunhofer Institute for Algorithms and Scientific Computing SCAI, PDTec AG, TU Berlin, TU München, Volkswagen AG

Teaching

Awards

Publications

[1] B. Bohn. Error analysis of regularized and unregularized least-squares regression on discretized function spaces. Dissertation, Institut für Numerische Simulation, Universität Bonn, Jan. 2017.
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[2] B. Bohn. On the convergence rate of sparse grid least squares regression. Lecture Notes in Computational Science and Engineering. Springer, 2017. submitted to Proceedings of the fourth workshop on sparse grids and applications, also available as INS Preprint No. 1711.
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[3] B. Bohn and M. Griebel. Error estimates for multivariate regression on discretized function spaces. SIAM Journal on Numerical Analysis, 55(4):1843-1866, 2017. also available as INS Preprint No. 1412.
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[4] B. Bohn, M. Griebel, and C. Rieger. A representer theorem for deep kernel learning. 2017. also available as INS Preprint No. 1714.
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[5] B. Bohn, J. Garcke, and M. Griebel. A sparse grid based method for generative dimensionality reduction of high-dimensional data. Journal of Computational Physics, 309:1-17, 2016. also available as INS Preprint No. 1514.
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[6] B. Bohn, J. Garcke, R. Iza-Teran, A. Paprotny, B. Peherstorfer, U. Schepsmeier, and C.-A. Thole. Analysis of Car Crash Simulation Data with Nonlinear Machine Learning Methods. Procedia Computer Science, 18:621 - 630, 2013. Proceedings of the International Conference on Computational Science, {ICCS} 2013.
bib | DOI | http ]
[7] B. Bohn and M. Griebel. An adaptive sparse grid approach for time series predictions. In J. Garcke and M. Griebel, editors, Sparse grids and Applications, volume 88 of Lecture Notes in Computational Science and Engineering, pages 1-30. Springer, 2012. Also available as INS Preprint no 1201.
bib | DOI | .pdf 1 ]
[8] B. Bohn. Einbettung von Zeitreihen nach Takens' Theorem. Diplomarbeit, Institut für Numerische Simulation, Universität Bonn, Mar. 2010.
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[9] M. Steffens, T. Becker, T. Sander, R. Fimmers, C. Herold, D. Holler, C. Leu, S. Herms, S. Cichon, B. Bohn, T. Gerstner, M. Griebel, M. Nöthen, T. Wienker, and M. Baur. Feasible and successful: Genome-wide Interaction Analysis (GWIA) involving all 1.9x1011 pair-wise interaction tests. Human Heredity, 69:268-284, 2010.
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Talks

Conference contributions

[1] Sparse grid regression in the noiseless setting,
SGA16 - 4th Workshop on Sparse Grids and Applications, Miami, FL, USA, October 4-7, 2016.
[2] Error bounds for regularized least-squares regression on finite-dimensional function spaces,
UQ16 - SIAM Conference on Uncertainty Quantification, Lausanne, Switzerland, April 5-8, 2016.
[3] Convergence results for discretized regression applied to sparse grids,
SGA14 - 3rd Workshop on Sparse Grids and Applications, Stuttgart, Germany, September 1-5, 2014.
[4] Hierarchical dimension-adaptive machine learning,
MC-QMC2014 - Eleventh International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing, Leuven, Belgium, April 6-11, 2014.
[5] SIMDATA-NL: Nichtlineare Charakterisierung und Analyse von FEM-Simulationsergebnissen für Autobauteile und Crash-Tests,
BMBF Statusseminar "Mathematik für Innovationen in Industrie und Dienstleistungen", Bonn, Germany, June 20-21, 2013.
[6] On dimensionality reduction for high-dimensional machine learning with sparse grids,
HDA2013 - 5th Workshop on High-Dimensional Approximation, Canberra, Australia, February 11-15, 2013.
[7] Adaptive Sparse Grid Discretizations for High-Dimensional Manifold Learning Tasks,
DWCAA 2012 - 3rd Dolomites Workshop on Constructive Approximation and Applications, Alba di Canazei, Italy, September 9-14, 2012.
[8] Principal Manifold Learning with a Dimension-Adaptive Sparse Grid Discretization,
2nd Workshop on Sparse Grids and Applications, Munich, Germany, July 2-6, 2012.
[9] An Adaptive Sparse Grid Approach to Time Series Prediction,
HIM Trimester Program on Analysis and Numerics for High Dimensional Problems: Workshop on Sparse Grids and Applications, Bonn, Germany, May 16-20, 2011.