# Publications

## Recent PrePrints

**Wittwar, D. & Haasdonk, B.:**Greedy Algorithms for Matrix-Valued Kernels,

*University of Stuttgart,*

**2018**.

**Alla, A.; Haasdonk, B. & Schmidt, A.:**Feedback control of parametrized PDEs via model order reduction and dynamic programming principle,

*University of Stuttgart,*

**2017**.

**Köppl, T.; Santin, G.; Haasdonk, B. & Helmig, R.:**Numerical modelling of a peripheral arterial stenosis using dimensionally reduced models and machine learning techniques,

*University of Stuttgart,*

**2017**.

**Köppel, M.; Franzelin, F.; Kröker, I.; Oladyshkin, S.; Santin, G.; Wittwar, D.; Barth, A.; Haasdonk, B.; Nowak, W.; Pflüger, D. & Rohde, C.:**Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario,

*University of Stuttgart, (submitted),*

**2017**.

**Schmidt, A. & Haasdonk, B.:**Data-driven surrogates of value functions and applications to feedback control for dynamical systems,

*University of Stuttgart,*

**2017**.

**Wittwar, D.; Schmidt, A. & Haasdonk, B.:**Reduced Basis Approximation for the Discrete-time Parametric Algebraic Riccati Equation,

*University of Stuttgart,*

**2017**.

**Carlberg, K.; Brencher, L.; Haasdonk, B. & Barth, A.:**Data-driven time parallelism via forecasting,

**2016**.

**Fritzen, F.; Haasdonk, B.; Ryckelynck, D. & Schöps, S.:**An algorithmic comparison of the Hyper-Reduction and the Discrete Empirical Interpolation Method for a nonlinear thermal problem,

*University of Stuttgart,*

**2016**.

## Publications

**Bhatt, A.; Fehr, J. & Hassdonk, B.:**Model Order Reduction of an Elastic Body under Large Rigid Motion,

*Proceedings of ENUMATH 2017, Voss, Norway,*

**2018**.

**Brünnette, T.; Santin, G. & Haasdonk, B.:**Greedy kernel methods for accelerating implicit integrators for parametric ODEs,

*University of Stuttgart,*

**2018**

*, Proceedings of ENUMATH 2017*.

**De Marchi, S.; Iske, A. & Santin, G.:**Image reconstruction from scattered Radon data by weighted positive definite kernel functions,

*Calcolo,*

**2018**

*, 55*, 2.

**Fritzen, F.; Haasdonk, B.; Ryckelynck, D. & Schöps, S.:**An algorithmic comparison of the Hyper-Reduction and the Discrete Empirical Interpolation Method for a nonlinear thermal problem,

*Math. Comput. Appl. 2018,*

*University of Stuttgart,*

**2018**

*, 23*.

**Alla, A.; Schmidt, A. & Haasdonk, B.:**

*Benner, Peter and Ohlberger, Mario and Patera, Anthony and Rozza, Gianluigi and Urban, Karsten*

*(Eds.)*, Model Order Reduction Approaches for Infinite Horizon Optimal Control Problems via the HJB Equation,

*Model Reduction of Parametrized Systems,*

*Springer International Publishing,*

**2017**, 333-347.

**De Marchi, S.; Idda, A. & Santin, G.:**

*Fasshauer, Gregory E. and Schumaker, Larry L.*

*(Eds.)*, A Rescaled Method for RBF Approximation,

*Approximation Theory XV: San Antonio 2016,*

*Springer International Publishing,*

**2017**, 39-59.

**Dibak, C.; Schmidt, A.; Dürr, F.; Haasdonk, B. & Rothermel, K.:**Server-Assisted Interactive Mobile Simulations for Pervasive Applications,

*Proceedings of the 15th IEEE International Conference on Pervasive Computing and Communications (PerCom),*

*University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Germany,*

*IEEE,*

**2017**, 1-10.

**Fehr, J.; Grunert, D.; Bhatt, A. & Hassdonk, B.:**A Sensitivity Study of Error Estimation in Reduced Elastic Multibody Systems,

*Proceedings of MATHMOD 2018, Vienna, Austria,*

**2017**.

**Haasdonk, B.:**

*P. Benner and A. Cohen and M. Ohlberger and K. Willcox*

*(Eds.)*, Reduced Basis Methods for Parametrized PDEs -- A Tutorial Introduction for Stationary and Instationary Problems,

*Model Reduction and Approximation: Theory and Algorithms,*

*SIAM, Philadelphia,*

**2017**, 65-136.

**Haasdonk, B. & Santin, G.:**Greedy Kernel Approximation for Sparse Surrogate Modelling,

*Proceedings of the KoMSO Challenge Workshop on Reduced-Order Modeling for Simulation and Optimization,*

**2017**.

**Martini, I.; Rozza, G. & Haasdonk, B.:**Certified Reduced Basis Approximation for the Coupling of Viscous and Inviscid Parametrized Flow Models,

*Journal of Scientific Computing,*

**2017**.

**Santin, G. & Haasdonk, B.:**Convergence rate of the data-independent P-greedy algorithm in kernel-based approximation,

*Dolomites Research Notes on Approximation,*

**2017**

*, 10*, 68-78.

**Schmidt, A. & Haasdonk, B.:**Reduced basis approximation of large scale parametric algebraic Riccati equations,

*ESAIM: Control, Optimisation and Calculus of Variations,*

*EDP Sciences,*

**2017**.

**Tempel, P.; Schmidt, A.; Haasdonk, B. & Pott, A.:**Application of the Rigid Finite Element Method to the Simulation of Cable-Driven Parallel Robots,

*Computational Kinematics,*

*Springer International Publishing,*

**2017**, 198-205.

**Amsallem, D. & Haasdonk, B.:**PEBL-ROM: Projection-Error Based Local Reduced-Order Models,

*AMSES, Advanced Modeling and Simulation in Engineering Sciences,*

**2016**

*, 3*.

**Antoulas, A. C.; Haasdonk, B. & Peherstorfer, B.:**MORML 2016 Book of Abstracts,

*University of Stuttgart,*

**2016**.

**Baur, U.; Benner, P.; Haasdonk, B.; Himpe, C.; Maier, I. & Ohlberger, M.:**

*P. Benner and A. Cohen and M. Ohlberger and K. Willcox*

*(Eds.)*, Comparison of methods for parametric model order reduction of instationary problems,

*Model Reduction and Approximation for Complex Systems,*

*Birkhäuser Publishing,*

**2016**.

**Garmatter, D.; Haasdonk, B. & Harrach, B.:**A reduced Landweber Method for Nonlinear Inverse Problems,

*Inverse Problems,*

**2016**

*, 32*, 1-21.

**Redeker, M. & Haasdonk, B.:**A POD-EIM reduced two-scale model for precipitation in porous media,

*MCMDS, Mathematical and Computer Modelling of Dynamical Systems,*

**2016**.

**Schmidt, A. & Haasdonk, B.:**Reduced basis method for H2 optimal feedback control problems,

*IFAC-PapersOnLine,*

**2016**

*, 49*, 327 - 332.

**Amsallem, D.; Farhat, C. & Haasdonk, B.:**Editorial: Special Issue on Modelling Reduction,

*IJNME, International Journal of Numerical Methods in Engineering,*

**2015**

*, 102*, 931-932.

**Burkovska, O.; Haasdonk, B.; Salomon, J. & Wohlmuth, B.:**Reduced basis methods for pricing options with the Black-Scholes and Heston model,

*SIAM journal on Financial Mathematics (SIFIN),*

*Arxiv,*

**2015**.

**Dihlmann, M. A. & Haasdonk, B.:**Certified PDE-constrained parameter optimization using reduced basis surrogate models for evolution problems,

*COAP, Computational Optimization and Applications,*

**2015**

*, 60*, 753-787.

**Dihlmann, M. & Haasdonk, B.:**A reduced basis Kalman filter for parametrized partial differential equations,

*ESAIM: Control, Optimisation and Calculus of Variations,*

*EDP Sciences,*

**2015**.

**Kaulmann, S.; Flemisch, B.; Haasdonk, B.; Lie, K.-A. & Ohlberger, M.:**The Localized Reduced Basis Multiscale method for two-phase flows in porous media,

*Internat. J. Numer. Methods Engrg.,*

**2015**

*, 102*, 1018-1040.

**Martini, I. & Haasdonk, B.:**Output Error Bounds for the Dirichlet-Neumann Reduced Basis Method,

*Numerical Mathematics and Advanced Applications - ENUMATH 2013,*

**2015**

*, 103*, 437-445.

**Martini, I.; Rozza, G. & Haasdonk, B.:**Reduced basis approximation and a-posteriori error estimation for the coupled Stokes-Darcy system,

*Advances in Computational Mathematics,*

**2015**

*, 41*, 1131-1157.

**Redeker, M. & Haasdonk, B.:**A POD-EIM reduced two-scale model for crystal growth,

*Advances in Computational Mathematics,*

*Springer US,*

**2015**

*, 41*, 987-1013.

**Schmidt, A.; Dihlmann, M. & Haasdonk, B.:**Basis generation approaches for a reduced basis linear quadratic regulator,

*Proc. MATHMOD 2015 - 8th Vienna International Conference on Mathematical Modelling,*

**2015**, 713-718.

**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.

**Haasdonk, B. & Ohlberger, M.:**Wenn die Probleme zahlreicher werden: Reduzierte Basis Methoden für effiziente und gesicherte numerische Simulation,

*GAMM Rundbrief,*

**2014**

*, 2014*, 6-13.

**Kaulmann, S.; Flemisch, B.; Haasdonk, B.; Lie, K.-A. & Ohlberger, M.:**The Localized Reduced Basis Multiscale method for two-phase flows in porous media,

*arXiv.org,*

**2014**.

**Maier, I. & Haasdonk, B.:**A Dirichlet-Neumann reduced basis method for homogeneous domain decomposition problems,

*Applied Numerical Mathematics,*

**2014**

*, 78*, 31-48.

**Wirtz, D.; Sorensen, D. & Haasdonk, B.:**A Posteriori Error Estimation for DEIM Reduced Nonlinear Dynamical Systems,

*SIAM Journal on Scientific Computing,*

*Society for Industrial & Applied Mathematics (SIAM),*

**2014**

*, 36*, A311-A338.

**Wirtz, D.; Sorensen, D. & Haasdonk, B.:**A-posteriori error estimation for DEIM reduced nonlinear dynamical systems,

*SIAM J. Sci. Comp.,*

*University of Stuttgart,*

**2014**

*, 36*, A311-A338.

**Amsallem, D.; Haasdonk, B. & Rozza, G.:**A Conference within a Conference for MOR Researchers,

*SIAM News,*

**2013**

*, 46*, 8.

**Dihlmann, M. & Haasdonk, B.:**Certified Nonlinear Parameter Optimization with Reduced Basis Surrogate Models,

*PAMM, Proc. Appl. Math. Mech., Special Issue: 84th Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM), Novi Sad 2013; Editors: L. Cvetković, T. Atanacković and V. Kostić,*

**2013**

*, 13*, 3-6.

**Fehr, J.; Fischer, M.; Haasdonk, B. & Eberhard, P.:**Greedy-based Approximation of Frequency-weighted Gramian Matrices for Model Reduction in Multibody Dynamics,

*ZAMM,*

**2013**

*, 93*, 501-519.

**Haasdonk, B.:**Convergence Rates of the POD--Greedy Method,

*ESAIM: Mathematical Modelling and Numerical Analysis,*

*EDP Sciences,*

**2013**

*, 47*, 859-873.

**Haasdonk, B.; Urban, K. & Wieland, B.:**Reduced basis methods for parametrized partial differential equations with stochastic influences using the Karhunen Loeve expansion,

*SIAM/ASA J. Unc. Quant.,*

**2013**

*, 1*, 79-105.

**Kaulmann, S. & Haasdonk, B.:**

*Moitinho de Almeida, José Paulo Baptista and Diez, Pedro and Tiago, Carlos and Parés, Núria*

*(Eds.)*, Online Greedy Reduced Basis Construction Using Dictionaries,

*VI International Conference on Adaptive Modeling and Simulation (ADMOS 2013),*

**2013**, 365-376.

**Wirtz, D. & Haasdonk, B.:**A Vectorial Kernel Orthogonal Greedy Algorithm,

*Dolomites Res. Notes Approx.,*

**2013**

*, 6*, 83-100.

**Albrecht, F.; Haasdonk, B.; Kaulmann, S. & Ohlberger, M.:**

*Handloviv cová, Angela and Minarechová, Zuzana and v Sevv coviv c, Daniel*

*(Eds.)*, The Localized Reduced Basis Multiscale Method,

*Algoritmy 2012,*

*Publishing House of STU,*

**2012**, 393-403.

**Dihlmann, M.; Kaulmann, S. & Haasdonk, B.:**Online Reduced Basis Construction Procedure for Model Reduction of Parametrized Evolution Systems,

*Proc. MATHMOD 2012 - 7th Vienna International Conference on Mathematical Modelling,*

**2012**.

**Drohmann, M.; Haasdonk, B. & Ohlberger, M.:**Reduced Basis Model Reduction of Parametrized Two-phase Flow in Porous Media,

*Proc. MATHMOD 2012 - 7th Vienna International Conference on Mathematical Modelling,*

**2012**.

**Drohmann, M.; Haasdonk, B. & Ohlberger, M.:**Reduced Basis Approximation for Nonlinear Parametrized Evolution Equations based on Empirical Operator Interpolation,

*SIAM J. Sci. Comput.,*

**2012**

*, 34*, A937-A969.

**Drohmann, M.; Haasdonk, B. & Ohlberger, M.:**

*Dedner, Andreas and Flemisch, Bernd and Klöfkorn, Robert*

*(Eds.)*, A Software Framework for Reduced Basis Methods Using DUNE-RB and RBMATLAB,

*Advances in DUNE: Proceedings of the DUNE User Meeting, Held in October 6th-8th 2010 in Stuttgart, Germany,*

*Springer,*

**2012**.

**Haasdonk, B.; Salomon, J. & Wohlmuth, B.:**A Reduced Basis Method for Parametrized Variational Inequalities,

*SIAM Journal on Numerical Analysis,*

**2012**

*, 50*, 2656-2676.

**Haasdonk, B.; Salomon, J. & Wohlmuth, B.:**A Reduced Basis Method for the Simulation of American Options,

*ENUMATH 2011 Proceedings,*

**2012**.

**Ruiner, T.; Fehr, J.; Haasdonk, B. & Eberhard, P.:**A-posteriori error estimation for second order mechanical systems,

*Acta Mechanica Sinica,*

**2012**

*, 28(3)*, 854-862.

**Waldherr, S. & Haasdonk, B.:**Efficient Parametric Analysis of the Chemical Master Equation through Model Order Reduction,

*BMC Systems Biology,*

**2012**

*, 6*, 81.

**Wirtz, D. & Haasdonk, B.:**A-posteriori error estimation for parameterized kernel-based systems,

*Proc. MATHMOD 2012 - 7th Vienna International Conference on Mathematical Modelling,*

**2012**.

**Wirtz, D. & Haasdonk, B.:**Efficient a-posteriori error estimation for nonlinear kernel-based reduced systems,

*Systems and Control Letters,*

**2012**

*, 61*, 203 - 211.

**Dihlmann, M.; Drohmann, M. & Haasdonk, B.:**Model Reduction of Parametrized Evolution Problems using the Reduced basis Method with Adaptive Time-Partitioning,

*Proc. of ADMOS 2011,*

**2011**.

**Drohmann, M.; Haasdonk, B. & Ohlberger, M.:**Adaptive Reduced Basis Methods for Nonlinear Convection-Diffusion Equations,

*In Proc. FVCA6,*

**2011**.

**Haasdonk, B.; Dihlmann, M. & Ohlberger, M.:**A Training Set and Multiple Basis Generation Approach for Parametrized Model Reduction Based on Adaptive Grids in Parameter Space,

*Mathematical and Computer Modelling of Dynamical Systems,*

**2011**

*, 17*, 423-442.

**Haasdonk, B. & Lohmann, B.:**Special Issue on ''Model Order Reduction of Parametrized Problems'',

*Mathematical and Computer Modelling of Dynamical Systems,*

**2011**

*, 17*, 295-296.

**Haasdonk, B. & Ohlberger, M.:**Efficient reduced models and it a posteriori error estimation for parametrized dynamical systems by offline/online decomposition,

*Math. Comput. Model. Dyn. Syst.,*

**2011**

*, 17*, 145-161.

**Jung, N.; Patera, A.; Haasdonk, B. & Lohmann, B.:**Model Order Reduction and Error Estimation with an Application to the Parameter-Dependent Eddy Current Equation,

*Mathematical and Computer Modelling of Dynamical Systems,*

**2011**

*, 17*, 561-582.

**Kaulmann, S.; Ohlberger, M. & Haasdonk, B.:**A new local reduced basis discontinuous Galerkin approach for heterogeneous multiscale problems,

*Comptes Rendus Mathematique,*

**2011**

*, 349*, 1233-1238.

**Haasdonk, B.:**Effiziente und Gesicherte Modellreduktion für Parametrisierte Dynamische Systeme.,

*at - Automatisierungstechnik,*

**2010**

*, 58*, 468-474.

**Pekalska, E. & Haasdonk, B.:**Indefinite Kernel Discriminant Analysis,

*Proc. COMPSTAT 2010, International Conference on Computational Statistics,*

**2010**.

**Haasdonk, B. & Ohlberger, M.:**Space-Adaptive Reduced Basis Simulation for Time-Dependent Problems,

*Proc. MATHMOD 2009, 6th Vienna International Conference on Mathematical Modelling,*

**2009**.

**Haasdonk, B. & Ohlberger, M.:**Efficient Reduced Models for Parametrized Dynamical Systems by Offline/Online Decomposition,

*Proc. MATHMOD 2009, 6th Vienna International Conference on Mathematical Modelling,*

**2009**.

**Haasdonk, B. & Ohlberger, M.:**Efficient a-posteriori Error Estimation for Parametrized Reduced Dynamical Systems,

*GMA-Fachaussschuss 1.30, Tagungsband,*

**2009**.

**Haasdonk, B. & Ohlberger, M.:**Reduced basis method for explicit finite volume approximations of nonlinear conservation laws,

*Hyperbolic problems: theory, numerics and applications,*

*Amer. Math. Soc.,*

**2009**

*, 67*, 605-614.

**Haasdonk, B.; Ohlberger, M.; Tonn, T. & Urban, K.:**MoRePaS 2009 Book of Abstracts,

*University of Münster,*

**2009**.

**Jung, N.; Haasdonk, B. & Kröner, D.:**Reduced Basis Method for Quadratically Nonlinear Transport Equations,

*IJCSM,*

**2009**

*, 2*, 334-353.

**Pekalska, E. & Haasdonk, B.:**Kernel Discriminant Analysis with Positive Definite and Indefinite Kernels,

*IEEE Transactions on Pattern Analysis and Machine Intelligence,*

**2009**

*, 31*, 1017-1032.

**Drohmann, M.; Haasdonk, B. & Ohlberger, M.:**Reduced Basis Method for Finite Volume Approximation of Evolution Equations on Parametrized Geometries,

*Proceedings of ALGORITMY 2009,*

**2008**, 111-120.

**Haasdonk, B. & Ohlberger, M.:**Reduced basis method for finite volume approximations of parametrized linear evolution equations,

*ESAIM: M2AN,*

**2008**

*, 42*, 277-302.

**Haasdonk, B. & Ohlberger, M.:**Adaptive basis enrichment for the reduced basis method applied to finite volume schemes,

*Finite volumes for complex applications V,*

*ISTE, London,*

**2008**, 471-478.

**Haasdonk, B.; Ohlberger, M. & Rozza, G.:**A Reduced Basis Method for Evolution Schemes with Parameter-Dependent Explicit Operators,

*ETNA, Electronic Transactions on Numerical Analysis,*

**2008**

*, 32*, 145-161.

**Haasdonk, B. & Pekalska, E.:**Indefinite Kernel Fisher Discriminant,

*Proc. ICPR 2008, International Conference on Pattern Recognition,*

**2008**.

**Haasdonk, B. & Pekalska, E.:**Classification with Kernel Mahalanobis Distances,

*Proc. of 32nd. GfKl Conference, Advances in Data Analysis, Data Handling and Business Intelligence,*

**2008**.

**Fuhrmann J; Haasdonk, B.; Holzbecher, E. & Ohlberger, M.:**Guest Editorial for Special Issue on Modelling and Simulation of PEM-FC,

*Journal of Fuel Cell Science and Technology,*

**2007**.

**Haasdonk, B. & Burkhardt, H.:**Classification with Invariant Distance Substitution Kernels,

*Proc. of 31st GfKl Conference, Data Analysis, Machine Learning, and Applications,*

**2007**.

**Haasdonk, B. & Burkhardt, H.:**Invariant Kernels for Pattern Analysis and Machine Learning,

*Machine Learning,*

*IIF-LMB, Universität Freiburg, Institut für Informatik,*

**2007**

*, 68*, 35-61.

**Haasdonk, B. & Ohlberger, M.:**

*P. Díez and K. Runesson*

*(Eds.)*, Basis Construction for Reduced Basis Methods By Adaptive Parameter Grids,

*Proc. International Conference on Adaptive Modeling and Simulation, ADMOS 2007,*

*CIMNE, Barcelona,*

**2007**.

**Peschke, K.-D.; Haasdonk, B.; Ronneberger, O.; Burkhard, H.; Rösch, P.; Harz, M. & Popp, J.:**Using Transformation Knowledge for the Classification of Raman Spectra of Biological Samples,

*BIOMED 2006, Proc. of the 4th IASTED International Conference on Biomedical Engineering,*

**2006**, 288-293.

**Haasdonk, B.:**Feature Space Interpretation of SVMs with Indefinite Kernels,

*IEEE Transactions on Pattern Analysis and Machine Intelligence,*

*IEEE Computer Society,*

**2005**

*, 27*, 482-492.

**Haasdonk, B.; Vossen, A. & Burkhardt, H.:**Invariance in Kernel Methods by Haar-Integration Kernels,

*Proceedings of the 14th Scandinavian Conference on Image Analysis,*

*Springer,*

**2005**.

**Haasdonk, B. & Bahlmann, C.:**Learning with Distance Substitution Kernels,

*Pattern Recognition - Proceedings of the 26th DAGM Symposium,*

*Springer,*

**2004**, 220-227.

**Haasdonk, B.; Halawani, A. & Burkhardt, H.:**Adjustable invariant features by partial Haar-integration,

*Proceedings of the 17th International Conference on Pattern Recognition,*

**2004**

*, 2*, 769- 774.

**Haasdonk, B.; Ohlberger, M.; Rumpf, M.; Schmidt, A. & Siebert, K. G.:**Multiresolution Visualization of Higher Order Adaptive Finite Element Simulations,

*Computing,*

**2003**

*, 70*, 181-204.

**Bahlmann, C.; Haasdonk, B. & Burkhardt, H.:**On-line Handwriting Recognition with Support Vector Machines - A Kernel Approach,

*Proc. of the 8th International Workshop on Frontiers in Handwriting Recognition,*

*IEEE Computer Society,*

**2002**, 49-54.

**Haasdonk, B. & Keysers, D.:**Tangent Distance Kernels for Support Vector Machines,

*Proceedings of the 16th International Conference on Pattern Recognition,*

*IEEE Computer Society,*

**2002**

*, 2*, 864-868.

**Haasdonk, B.; Kröner, D. & Rohde, C.:**Convergence of a staggered Lax-Friedrichs scheme for nonlinear conservation laws on unstructured two-dimensional grids,

*Numer. Math.,*

**2001**

*, 88*, 459-484.

**Haasdonk, B.:**Convergence of a Staggered Lax-Friedrichs Scheme on Unstructured 2D-Grids,

*HYP 2000, Proceedings of the 8th International Conference on Hyperbolic Problems,*

*Birkhäuser,*

**2000**

*, 2*, 475-484.