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]
Computational Efficiency vs. Maintainability and Portability. Experiences with the Sparse Grid Code SG++. 2016 Fourth International Workshop on Software Engineering for High Performance Computing in Computational Science and Engineering (SE-HPCCSE), 17--25, IEEE, Salt Lake City, UT, USA, November 2016. [PUMA: Computational_modeling Hardware Programming Software_engineering Software_quality Usability computational_efficiency computational_maintainability computational_portability design_decisions from:leiterrl software_maintenance software_quality sparse_grid_code_SG++] URL
Automatic Safety Computation for IMA Systems. IEEE/AIAA 30th Digital Avionics Systems Conference (DASC), 7C3-1-7C3-9, Seattle, Oktober 2011. [PUMA: Aerospace_electronics Atmospheric_modeling Computational_modeling Computers Hardware IMA_systems InProceedings Redundancy Safety aerospace_safety aircraft_manufacturers automatic_safety_computation avionics ils integrated_modular_avionics_design_philosophy optimisation optimisation_algorithm safety_requirements] URL