Publications

Yuancong Gong, Andreas Gneiting, Chongshen Zhao, Nejila Parspour, and Hao Chen. Comparative Study of Different Data-Driven Surrogate Models for Optimization of Synchronous Reluctance Machine. IEEE Transactions on Industry Applications, 1--13, 2025. [PUMA: Computational_modeling Convolution Convolutional_neural_networks Kernel Kriging Mathematical_models Neurons Synchronous_Reluctance_Machine Topology Training convolutional_neural_network data-driven_surrogate_model drive_cycle_optimization fully_connected_deep_neural_network hp_iew operating_point_dependent_optimization optimization support_vector_regression torque]

A. Schmidt, and Bernard Haasdonk. Data-driven surrogates of value functions and applications to feedback control for dynamical systems. IFAC-PapersOnLine, (51)2:307--312, 2018. [PUMA: Kernel anm approximation, control, dynamic feedback from:britsteiner greedy ians optimal principle, programming techniques] URL

Tobias Köppl, Gabriele Santin, Bernard Haasdonk, and Rainer Helmig. Numerical modelling of a peripheral arterial stenosis using dimensionally reduced models and kernel methods. International Journal for Numerical Methods in Biomedical Engineering, (34)8:e3095, 2018. [PUMA: anm blood dimensionally flow from:britsteiner ians kernel methods, mixed‐dimension models, peripheral real‐time reduced simulations simulations, stenosis, surrogate] URL

Daniel Wirtz, and Bernard Haasdonk. Efficient a-posteriori error estimation for nonlinear kernel-based reduced systems. Systems & Control Letters, (61)1:203--211, Elsevier BV, 2012. [PUMA: subspace error dynamical kernel a-posteriori from:britsteiner methods, ians nonlinear systems, offline/online decomposition, projection estimates, model anm reduction,] URL

Daniel Wirtz, and Bernard Haasdonk. A-posteriori error estimation for parameterized kernel-based systems. Proc. MATHMOD 2012 - 7th Vienna International Conference on Mathematical Modelling, 2012. [PUMA: a-posteriori anm decomposition, dynamical error estimates, ians kernel methods, model nonlinear offline/online parameterized projection reduction, subspace systems,] URL

Tobias Köppl, Gabriele Santin, Bernard Haasdonk, and Rainer Helmig. Numerical modelling of a peripheral arterial stenosis using dimensionally reduced models and kernel methods. International Journal for Numerical Methods in Biomedical Engineering, (34)8:e3095, 2018. [PUMA: anm blood dimensionally flow ians kernel methods, mixed‐dimension models, peripheral real‐time reduced simulations simulations, stenosis, surrogate] URL

A. Schmidt, and Bernard Haasdonk. Data-driven surrogates of value functions and applications to feedback control for dynamical systems. IFAC-PapersOnLine, (51)2:307--312, 2018. [PUMA: Kernel anm approximation, control, dynamic feedback greedy ians optimal principle, programming techniques] URL

Daniel Wirtz, and Bernard Haasdonk. Efficient a-posteriori error estimation for nonlinear kernel-based reduced systems. Systems & Control Letters, (61)1:203--211, Elsevier BV, 2012. [PUMA: a-posteriori anm decomposition, dynamical error estimates, ians kernel methods, model nonlinear offline/online projection reduction, subspace systems,] URL

D. Wirtz, and B. Haasdonk. Efficient a-posteriori error estimation for nonlinear kernel-based reduced systems. Systems and Control Letters, (61)1:203 - 211, 2012. [PUMA: a-posteriori decomposition, dynamical error estimates, from:mhartmann ians kernel methods, model nonlinear offline/online projection reduction, subspace systems, vorlaeufig] URL

Tobias Köppl, Gabriele Santin, Bernard Haasdonk, and Rainer Helmig. Numerical modelling of a peripheral arterial stenosis using dimensionally reduced models and kernel methods. International Journal for Numerical Methods in Biomedical Engineering, (0)ja:e3095, 2018. [PUMA: blood dimensionally flow from:mhartmann ians kernel methods, mixed-dimension models, peripheral real-time reduced simulations simulations, stenosis, surrogate vorlaeufig] URL

Daniel Wirtz, and Bernard Haasdonk. A-posteriori error estimation for parameterized kernel-based systems. Proc. MATHMOD 2012 - 7th Vienna International Conference on Mathematical Modelling, 2012. [PUMA: a-posteriori decomposition, dynamical error estimates, from:mhartmann ians kernel methods, model nonlinear offline/online parameterized projection reduction, subspace systems, vorlaeufig] URL

D. Wirtz, and B. Haasdonk. Efficient a-posteriori error estimation for nonlinear kernel-based reduced systems. Systems and Control Letters, (61)1:203 - 211, 2012. [PUMA: a-posteriori decomposition, dynamical error estimates, kernel methods, model nonlinear offline/online projection reduction, subspace systems, vorlaeufig] URL

Tobias Köppl, Gabriele Santin, Bernard Haasdonk, and Rainer Helmig. Numerical modelling of a peripheral arterial stenosis using dimensionally reduced models and kernel methods. International Journal for Numerical Methods in Biomedical Engineering, (0)ja:e3095, 2018. [PUMA: blood dimensionally flow kernel methods, mixed-dimension models, peripheral real-time reduced simulations simulations, stenosis, surrogate vorlaeufig] URL

Daniel Wirtz, and Bernard Haasdonk. A-posteriori error estimation for parameterized kernel-based systems. Proc. MATHMOD 2012 - 7th Vienna International Conference on Mathematical Modelling, 2012. [PUMA: a-posteriori decomposition, dynamical error estimates, kernel methods, model nonlinear offline/online parameterized projection reduction, subspace systems, vorlaeufig] URL