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Gelß, P. and Issagali, A. and Kornhuber, R. (2023) Fredholm integral equations for function approximation and the training of neural networks. arXiv . (Submitted)

Gelß, P. and Matera, S. and Klein, R. and Schmidt, B. (2023) Quantum Dynamics of Coupled Excitons and Phonons in Chain-Like Systems: Tensor Train Approaches and Higher-Order Propagators. J. Chem. Phys. . (Submitted)

Riedel, J. and Gelß, P. and Klein, R. and Schmidt, B. (2023) WaveTrain: A Python Package for Numerical Quantum Mechanics of Chain-Like Systems Based on Tensor Trains. J. Chem. Phys. (164801), 158 (16). ISSN 0021-9606

Gelß, P. and Klein, R. and Matera, S. and Schmidt, B. (2022) Solving the time-independent Schrödinger equation for chains of coupled excitons and phonons using tensor trains. J. Chem. Phys., 156 (02). 024109.

Klus, S. and Gelß, P. and Peitz, S. and Schütte, Ch. (2018) Tensor-based dynamic mode decomposition. Nonlinearity, 31 (7). pp. 3359-3380. ISSN 0951-7715

Gelß, P. and Klus, S. and Matera, S. and Schütte, Ch. (2017) Nearest-neighbor interaction systems in the tensor-train format. Journal of Computational Physics, 341 . pp. 140-162. ISSN 0021-9991

Gelß, P. and Matera, S. and Schütte, Ch. (2016) Solving the master equation without kinetic Monte Carlo: tensor train approximations for a CO oxidation model. Journal of Computational Physics, 314 . pp. 489-502. ISSN 0021-9991

Klus, S. and Gelß, P. and Peitz, S. and Schütte, Ch. (2016) Tensor-based dynamic mode decomposition. SIAM Journal on Scientific Computing . ISSN ISSN 1064-8275 (print); 1095-7197 (electronic) (Submitted)