Loukrezis, Dimitrios ; Diehl, Eric ; De Gersem, Herbert (2025): Multivariate sensitivity-adaptive polynomial chaos expansion for high-dimensional surrogate modeling and uncertainty quantification. In: Applied Mathematical Modelling, 137, pp. 115746, ISSN: 0307-904X, DOI: 10.1016/j.apm.2024.115746. [Article]
Partovizadeh, Aylar ; Schöps, Sebastian ; Loukrezis, Dimitrios (2025): Fourier-enhanced reduced-order surrogate modeling for uncertainty quantification in electric machine design. In: Engineering with Computers, DOI: 10.1007/s00366-025-02123-1, ARXIV: 2412.06485. [Article]
Fleig, Luisa ; Liebsch, Melvin ; Russenschuck, Stephan ; Schöps, Sebastian (2024): Identification of B(H) curves using the Karhunen Loève Expansion. In: IEEE Access, ISSN: 2169-3536, DOI: 10.1109/ACCESS.2024.3393348, ARXIV: 2306.12872. [Article]
Lippert, Jonathan Rainer ; von Tresckow, Moritz ; De Gersem, Herbert ; Loukrezis, Dimitrios (2024): Transfer learning‐based physics‐informed neural networks for magnetostatic field simulation with domain variations. In: International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, ISSN: 0894-3370, DOI: 10.1002/jnm.3264. [Article]
von Tresckow, Moritz ; De Gersem, Herbert ; Loukrezis, Dimitrios (2024): Error approximation and bias correction in dynamic problems using a recurrent neural network/finite element hybrid model. In: Applied Mathematical Modelling, 129, pp. 428–447, ISSN: 0307-904X, DOI: 10.1016/j.apm.2024.02.004, ARXIV: 2307.02349. [Article]
Diehl, Eric ; von Tresckow, Moritz ; Scholtissek, Lou ; Loukrezis, Dimitrios ; Marsic, Nicolas ; Müller, Wolfgang F. O. ; De Gersem, Herbert (2023): Quadrupole magnet design based on genetic multi-objective optimization. In: Electrical Engineering (Archiv für Elektrotechnik), 106, (2), pp. 1179–1189, ISSN: 1432-0487, DOI: 10.1007/s00202-023-02132-7, ARXIV: 2211.09580. [Article]
Galetzka, Armin ; Loukrezis, Dimitrios ; Georg, Niklas ; De Gersem, Herbert ; Römer, Ulrich (2023): An $hp$-adaptive multi-element stochastic collocation method for surrogate modeling with information re-use. In: International Journal for Numerical Methods in Engineering, 124, (12), pp. 2902–2930, ISSN: 0029-5981, DOI: 10.1002/nme.7234. [Article]
Parekh, Vivek ; Flore, Dominik ; Schöps, Sebastian (2023): Performance Analysis of Electrical Machines Using a Hybrid Data- and Physics-Driven Model. In: IEEE Transactions on Energy Conversion, ISSN: 0885-8969, DOI: 10.1109/TEC.2022.3209103, ARXIV: 2201.09603. [Article]
Huber, Morten ; Fuhrländer, Mona ; Schöps, Sebastian (2022): Multi-Objective Yield Optimization for Electrical Machines using Gaussian Processes to Learn Faulty Designs. In: IEEE Transactions on Industry Applications, 59, (2), pp. 1340–1350, ISSN: 0093-9994, DOI: 10.1109/TIA.2022.3211250, ARXIV: 2204.04986. [Article]
Ion, Ion Gabriel ; Loukrezis, Dimitrios ; De Gersem, Herbert (2022): Tensor train based isogeometric analysis for PDE approximation on parameter dependent geometries. In: Computer Methods in Applied Mechanics and Engineering, 401, (B), pp. 115593, ISSN: 0045-7825, DOI: 10.1016/j.cma.2022.115593, ARXIV: 2204.02843. [Article]
Loukrezis, Dimitrios ; De Gersem, Herbert (2022): Power module heat sink design optimization with data-driven polynomial chaos surrogate models. In: e-Prime – Advances in Electrical Engineering, Electronics and Energy, 2, pp. 100059, ISSN: 2772-6711, DOI: 10.1016/j.prime.2022.100059. [Article]
von Tresckow, Moritz ; Kurz, Stefan ; De Gersem, Herbert ; Loukrezis, Dimitrios (2022): A neural solver for variational problems on CAD geometries with application to electric machine simulation. In: Journal of Machine Learning for Modeling and Computing, 3, (1), pp. 49–75, ISSN: 2689-3967, DOI: 10.1615/JMachLearnModelComput.2022041753, ARXIV: 2111.09005. [Article]