List of Publications

W. Xiao, M. Besançon, P. Gelß, D. Hendrych, S. Klus, S. Pokutta. Graph Isomorphism: Mixed-Integer Convex Optimization from First-Order Methods. submitted. arXiv: 2512.17417 [BibTeX]

A. Sander, M. Fröhlich, M. Eigel, J. Eisert, P. Gelß, M. Hintermüller, R. M. Milbradt, R. Wille, C. B. Mendl. Large-scale stochastic simulation of open quantum systems. Nature Communications 16, 11074. DOI: 10.1038/s41467-025-66846-x, arXiv: 2501.17913 [BibTeX]

K. Khoruzhii, P. Gelß, S. Pokutta. Faster Algorithms for Structured Matrix Multiplication via Flip Graph Search. submitted. arXiv: 2511.10786 [BibTeX]

A. Kumar Sharma, C. Boehme, P. Gelß, R. Yahyapour, J. Kunkel. Workflow-driven modeling for the compute continuum: An optimization approach to automated system and workload scheduling. 2025 IEEE 49th Annual Computers, Software, and Applications Conference (COMPSAC). DOI: 10.1109/COMPSAC65507.2025.00343, arXiv: 2505.12184 [BibTeX]

S. Klus, P. Gelß, Continuous optimization methods for the graph isomorphism problem. Information and Inference: A Journal of the IMA 14 (2). DOI: 10.1093/imaiai/iaaf011, arXiv: 2311.16912 [BibTeX]

P. Gelß, R. Klein, S. Matera, and B. Schmidt. Quantum dynamics of coupled excitons and phonons in chain-like systems: tensor train approaches and higher-order propagators. Journal of Chemical Physics 162 (15). DOI: 10.1063/5.0258904, arXiv: 2302.03568 [BibTeX]

P. Gelß, A. Issagali, and R. Kornhuber. Fredholm integral equations for function approximation and the training of neural networks. SIAM Journal on Mathematics of Data Science 6 (4). DOI: 10.1137/23M156642X, arXiv: 2303.05262 [BibTeX]

S.-M. Stengl, P. Gelß, S. Klus, and S. Pokutta. Existence and Uniqueness of Solutions of the Koopman–von Neumann Equation on Bounded Domains. Journal of Physics A: Mathematical and Theoretical 57 (39). DOI: 10.1088/1751-8121/ad6f7d, arXiv: 2306.13504 [BibTeX]

S. Designolle, G. Iommazzo, M. Besancon, S. Knebel, P. Gelß, S. Pokutta. Improved local models and new Bell inequalities via Frank-Wolfe algorithms. Physical Review Research 5, 043059. DOI: 10.1103/PhysRevResearch.5.043059, arXiv: 2302.04721 [BibTeX]

J. Riedel, P. Gelß, R. Klein, and B. Schmidt. WaveTrain: A Python package for numerical quantum mechanics of chain-like systems based on tensor trains. Journal of Chemical Physics 158 (16). DOI: 10.1063/5.0147314, arXiv: 2302.03725 [BibTeX]

P. Gelß, S. Klus, S. Knebel, Z. Shakibaei, and S. Pokutta. Low-rank tensor decompositions of quantum circuits. arXiv: 2205.09882 [BibTeX]

P. Gelß, R. Klein, S. Matera, and B. Schmidt. 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, arXiv: 2109.15104 [BibTeX]

F. Nüske, P. Gelß, S. Klus, and C. Clementi. Tensor-based computation of metastable and coherent sets. Physica D 427, 133018. DOI: 10.1016/j.physd.2021.133018, arXiv: 1908.04741 [BibTeX]

S. Klus, P. Gelß, F. Nüske, and F. Noé. Symmetric and antisymmetric kernels for machine learning problems in quantum physics and chemistry. Machine Learning: Science and Technology 2 (4), 045016. DOI: 10.1088/2632-2153/ac14ad, arXiv: 2103.17233 [BibTeX]

P. Gelß, S. Klus, I. Schuster, and C. Schütte. Feature space approximation for kernel-based supervised learning. Knowledge-Based Systems 221, 106935. DOI: 10.1016/j.knosys.2021.106935, arXiv: 2011.12651 [BibTeX]

S. Klus and P. Gelß. Tensor-based algorithms for image classification. Algorithms 12 (11), 240. DOI: 10.3390/a12110240, arXiv: 1910.02150 [BibTeX]

P. Gelß, S. Klus, J. Eisert, and C. Schütte. Multidimensional approximation of nonlinear dynamical systems. Journal of Computational and Nonlinear Dynamics 14, 061006. DOI: 10.1115/1.4043148, arXiv:  1809.02448 [BibTeX]

P. Gelß and C. Schütte. Tensor-generated fractals: Using tensor decompositions for creating self-similar patterns. arXiv:  1812.00814 [BibTeX]

P. Gelß. The tensor-train format and its applications: Modeling and analysis of chemical reaction networks, catalytic processes, fluid flows, and Brownian dynamics. Dissertation, Freie Universität Berlin. DOI: 10.17169/refubium-7566 [BibTeX]

P. Gelß, S. Klus, S. Matera, and C. Schütte. Nearest-neighbor interaction systems in the tensor-train format. Journal of Computational Physics 341, pp. 140-162. DOI: 10.1016/j.jcp.2017.04.007, arXiv: 1611.03755 [BibTeX]

S. Klus, P. Gelß, S. Peitz, and C. Schütte. Tensor-based dynamic mode decomposition. Nonlinearity 31, 3359. DOI: 10.1088/1361-6544/aabc8f, arXiv: 1606.06625 [BibTeX]

P. Gelß, S. Matera, and C. Schütte. Solving the master equation without kinetic Monte Carlo: Tensor train approximations for a CO oxidation model. Journal of Computational Physics 314, pp. 489-502. DOI:  10.1016/j.jcp.2016.03.025 [BibTeX]