Group by:

Date |

Item TypeNumber of items: **8**.

## Article

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)

This list was generated on **Mon Aug 5 12:14:46 2024 CEST**.