Patrick Gelß
Zitiert von
Zitiert von
Mutational hierarchies in myelodysplastic syndromes dynamically adapt and evolve upon therapy response and failure
M Mossner, JC Jann, J Wittig, F Nolte, S Fey, V Nowak, J Obländer, ...
Blood, The Journal of the American Society of Hematology 128 (9), 1246-1259, 2016
Tensor-based dynamic mode decomposition
S Klus, P Gelß, S Peitz, C Schütte
Nonlinearity 31 (7), 3359, 2018
Multidimensional approximation of nonlinear dynamical systems
P Gelß, S Klus, J Eisert, C Schütte
Journal of Computational and Nonlinear Dynamics 14 (6), 061006, 2019
Solving the master equation without kinetic Monte Carlo: Tensor train approximations for a CO oxidation model
P Gelß, S Matera, C Schütte
Journal of Computational Physics 314, 489-502, 2016
Tensor-based algorithms for image classification
S Klus, P Gelß
Algorithms 12 (11), 240, 2019
The tensor-train format and its applications: Modeling and analysis of chemical reaction networks, catalytic processes, fluid flows, and Brownian dynamics
P Gelß
Nearest-neighbor interaction systems in the tensor-train format
P Gelß, S Klus, S Matera, C Schütte
Journal of Computational Physics 341, 140-162, 2017
The tensor-train format and its applications
P Gelß
PhD thesis, 2017
Tensor-based computation of metastable and coherent sets
F Nüske, P Gelß, S Klus, C Clementi
Physica D: Nonlinear Phenomena 427, 133018, 2021
Symmetric and antisymmetric kernels for machine learning problems in quantum physics and chemistry
S Klus, P Gelß, F Nüske, F Noé
Machine Learning: Science and Technology 2 (4), 045016, 2021
Solving the time-independent Schrödinger equation for chains of coupled excitons and phonons using tensor trains
P Gelß, R Klein, S Matera, B Schmidt
The Journal of Chemical Physics 156 (2), 2022
Improved local models and new Bell inequalities via Frank-Wolfe algorithms
S Designolle, G Iommazzo, M Besançon, S Knebel, P Gelß, S Pokutta
Physical Review Research 5 (4), 043059, 2023
Feature space approximation for kernel-based supervised learning
P Gelß, S Klus, I Schuster, C Schütte
Knowledge-Based Systems 221, 106935, 2021
Tensor-based EDMD for the Koopman analysis of high-dimensional systems
F Nüske, P Gelß, S Klus, C Clementi
arXiv preprint arXiv:1908.04741, 210, 2019
Low-rank tensor decompositions of quantum circuits
P Gelß, S Klus, S Knebel, Z Shakibaei, S Pokutta
arXiv preprint arXiv:2205.09882, 2022
Tensor-generated fractals: Using tensor decompositions for creating self-similar patterns
P Gelß, C Schütte
arXiv preprint arXiv:1812.00814, 2018
Low-rank approximability of nearest neighbor interaction systems
P Gelß, S Matera, R Schneider, A Uschmajew
WaveTrain: A Python package for numerical quantum mechanics of chain-like systems based on tensor trains
J Riedel, P Gelß, R Klein, B Schmidt
The Journal of Chemical Physics 158 (16), 2023
Fredholm integral equations for function approximation and the training of neural networks
P Gelß, A Issagali, R Kornhuber
arXiv preprint arXiv:2303.05262, 2023
Quantum dynamics of coupled excitons and phonons in chain-like systems: tensor train approaches and higher-order propagators
P Gelß, S Matera, R Klein, B Schmidt
arXiv preprint arXiv:2302.03568, 2023
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