Patrick Gelß
postdoctoral researcher at ZIB since July 2022📬 Contact
office | Room 1356 at ZIB |
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gelss (at) zib.de p.gelss (at) fu-berlin.de |
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homepage | patrickgelss.com |
languages | German and English |
🎓 Academic Background
Jun 2017 | Ph.D. in Applied Mathematics at FUB |
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Oct 2013 | Diploma in Mathematics at FUB |
🔬 Research
Preprints
- Stengl, S.-M., Gelß, P., Klus, S., and Pokutta, S. (2023). Existence and Uniqueness of Solutions of the Koopman–von Neumann Equation on Bounded Domains.
[arXiv]
[BibTeX]
- Gelß, P., Issagali, A., and Kornhuber, R. (2023). Fredholm Integral Equations for Function Approximation and the Training of Neural Networks.
[arXiv]
[BibTeX]
- Gelß, P., Klein, R., Matera, S., and Schmidt, B. (2023). Quantum Dynamics of Coupled Excitons and Phonons in Chain-like Systems: Tensor Train Approaches and Higher-order Propagators.
[arXiv]
[BibTeX]
- Klus, S., and Gelß, P. (2023). Continuous Optimization Methods for the Graph Isomorphism Problem.
[arXiv]
[BibTeX]
- Gelß, P., Klus, S., Shakibaei, Z., and Pokutta, S. (2022). Low-rank Tensor Decompositions of Quantum Circuits.
[arXiv]
[BibTeX]
- Gelß, P., and Schütte, C. (2018). Tensor-generated Fractals - Using Tensor Decompositions for Creating Self-similar Patterns.
[arXiv]
[BibTeX]
Full articles
- Designolle, S., Iommazzo, G., Besançon, M., Knebel, S., Gelß, P., and Pokutta, S. (2023). Improved Local Models and New Bell Inequalities Via Frank-Wolfe Algorithms. Physical Review Research, 5(4).
DOI: 10.1103/PhysRevResearch.5.043059
[arXiv]
[slides]
[code]
[BibTeX]
- Riedel, J., Gelß, P., Klein, R., and Schmidt, B. (2023). WaveTrain: A Python Package for Numerical Quantum Mechanics of Chain-like Systems Based on Tensor Trains. The Journal of Chemical Physics, 158(16), 164801.
DOI: 10.1063/5.0147314
[URL]
[arXiv]
[BibTeX]
- Gelß, P., Klein, R., Matera, S., and Schmidt, B. (2022). Solving the Time-independent Schrödinger Equation for Chains of Coupled Excitons and Phonons Using Tensor Trains. The Journal of Chemical Physics, 156, 024109.
DOI: 10.1063/5.0074948
[URL]
[arXiv]
[BibTeX]
- Gelß, P., Klus, S., Schuster, I., and Schütte, C. (2021). Feature Space Approximation for Kernel-based Supervised Learning. Knowledge-Based Systems, 221, 106935.
DOI: 10.1016/j.knosys.2021.106935
[URL]
[arXiv]
[BibTeX]
- Klus, S., Gelß, P., Nüske, F., and Noé, F. (2021). Symmetric and Antisymmetric Kernels for Machine Learning Problems in Quantum Physics and Chemistry. Machine Learning: Science and Technology, 2(4), 18958.
DOI: 10.1088/2632-2153/ac14ad
[URL]
[arXiv]
[BibTeX]
- Nüske, F., Gelß, P., Klus, S., and Clementi, C. (2021). Tensor-based Computation of Metastable and Coherent Sets. Physica D: Nonlinear Phenomena, 427, 133018.
DOI: 10.1016/j.physd.2021.133018
[URL]
[arXiv]
[BibTeX]
- Gelß, P., Klus, S., Eisert, J., and Schütte, C. (2019). Multidimensional Approximation of Nonlinear Dynamical Systems. Journal of Computational and Nonlinear Dynamics, 14(6), 25094.
DOI: 10.1115/1.4043148
[URL]
[arXiv]
[BibTeX]
- Klus, S., and Gelß, P. (2019). Tensor-based Algorithms for Image Classification. Algorithms, 12(11), 240.
DOI: 10.3390/a12110240
[URL]
[arXiv]
[BibTeX]
- Gelß, P., Klus, S., Matera, S., and Schütte, C. (2017). Nearest-neighbor Interaction Systems in the Tensor-train Format. Journal of Computational Physics, 341, 140–162.
DOI: 10.1016/j.jcp.2017.04.007
[URL]
[arXiv]
[BibTeX]
- Klus, S., Gelß, P., Peitz, S., and Schütte, C. (2017). Tensor-based Dynamic Mode Decomposition. Nonlinearity, 31(7), 3359.
DOI: 10.1088/1361-6544/aabc8f
[URL]
[arXiv]
[BibTeX]
- Gelß, P., Matera, S., and Schütte, C. (2016). Solving the Master Equation without Kinetic Monte Carlo - Tensor Train Approximations for a CO Oxidation Model. Journal of Computational Physics, 314, 489–502.
DOI: 10.1016/j.jcp.2016.03.025
[URL]
[BibTeX]
- Mossner, M., Jann, J.-C., Wittig, J., Nolte, F., Fey, S., Nowak, V., Obländer, J., Pressler, J., Palme, I., Xanthopoulos, C., Boch, T., Metzgeroth, G., Röhl, H., Witt, S. H., Dukal, H., Klein, C., Schmitt, S., Gelß, P., Platzbecker, U., … Nowak, D. (2016). Mutational Hierarchies in Myelodysplastic Syndromes Dynamically Adapt and Evolve Upon Therapy Response and Failure. Blood, 128(9), 1246–1259.
DOI: 10.1182/blood-2015-11-679167
[URL]
[BibTeX]
💬 Talks and posters
Conference and workshop talks
- Mar 2024
- GAMM Conference
- Feb 2024
- 7th SIAM UQ Conference
- Sep 2023
- 7th RIKEN-MODAL Workshop, Berlin
- Sep 2023
- 1st NHR Conference
- Jun 2023
- 1st Workshop on Quantum Computation and Optimization, Berlin
View More / Less
- Mar 2023
- 11th SIAM CSE Conference
- Sep 2022
- 6th RIKEN-MODAL Workshop, Tokyo / Fukuoka
- Jun 2022
- 24th ILAS Conference
- May 2022
- 3rd Workshop on Quantum Algorithms and Applications, Brussels
- Dec 2019
- Mathematics of Deep Learning
- Mar 2019
- 89th GAMM Conference
- Mar 2018
- DPG Spring Meeting of the Condensed Matter Section
- Aug 2017
- 7th International Symposium on Energy
- Mar 2017
- DPG Spring Meeting of the Condensed Matter Section
- Jan 2017
- Model Reduction of Complex Dynamical Systems
Research seminar talks
- Jan 2024
- IBM QCS seminar Seminar, Zurich
- Jul 2023
- IBM NOIP seminar Seminar, Berlin
- Jun 2023
- DESY CQTA seminar Seminar, Zeuthen
- Oct 2022
- Tensor Learning Team Seminar
👨🏫 Teaching
- summer 2022
- Lecturer for high-dimensional discretization at FUB
- winter 2021
- Tutor for functional analysis at FUB
- summer 2020
- Tutor for dynamical systems at FUB
- winter 2019
- Lecturer for functional analysis at FUB
- summer 2019
- Lecturer for tensor decompositions and their applications at FUB
- winter 2018
- Tutor for functional analysis at FUB