E-LIB Suche - Ergebnisse für: Grasedyck, Lars

1

Overcoming Observation Bias for Cancer Progression Modeling:

, In: Lecture Notes in Computer Science; Research in Computational Molecular Biology,

Schill, Rudolf ; Klever, Maren ; Lösch, Andreas... - p. 217-234 , 2024

Link: https://doi.org/10.1007/..

2

Reconstructing Disease Histories in Huge Discrete State Spa..:

Schill, Rudolf ; Klever, Maren ; Rupp, Kevin...
KI - Künstliche Intelligenz. , 2024

Link: https://doi.org/10.1007/..

3

Quasi-orthogonalization for alternating non-negative tensor..:

Grasedyck, Lars ; Klever, Maren ; Krämer, Sebastian
ETNA - Electronic Transactions on Numerical Analysis. 62 (2024) - p. 22-57 , 2024

Link: https://doi.org/10.1553/..

4

Modeling metastatic progression from cross-sectional cancer..:

Rupp, Kevin ; Lösch, Andreas ; Hu, Yanren Linda...
Bioinformatics. 40 (2024) Supplement_1 - p. i140-i150 , 2024

Link: https://doi.org/10.1093/..

5

Differentiated uniformization: a new method for inferring M..:

Rupp, Kevin ; Schill, Rudolf ; Süskind, Jonas...
Computational Statistics. , 2024

Link: https://doi.org/10.1007/..

6

Computing tensor operator exponentials within low‐rank tens..:

Grasedyck, Lars ; Werthmann, Tim A.
PAMM. 22 (2023) 1 - p. , 2023

Link: https://doi.org/10.1002/..

7

Applications of tensor networks in quantum chemistry and po..:

Götte, Michael , 2022

Link: https://nbn-resolving.de..

8

Low-rank tensor methods for Markov chains with applications..:

Georg, Peter ; Grasedyck, Lars ; Klever, Maren...
Journal of Mathematical Biology. 86 (2022) 1 - p. , 2022

Link: https://doi.org/10.1007/..

9

Numerical upscaling of parametric microstructures in a poss..:

Eigel, Martin ; Gruhlke, Robert ; Moser, Dieter.
Computational Mechanics. 71 (2022) 4 - p. 615-636 , 2022

Link: https://doi.org/10.1007/..

10

Rank Bounds for Approximating Gaussian Densities in the Ten..:

Rohrbach, Paul B. ; Dolgov, Sergey ; Grasedyck, Lars.
SIAM/ASA Journal on Uncertainty Quantification. 10 (2022) 3 - p. 1191-1224 , 2022

Link: https://doi.org/10.1137/..

11

A two-stage surrogate model for Neo-Hookean problems based ..:

Kastian, Steffen ; Moser, Dieter ; Grasedyck, Lars.
Computer Methods in Applied Mechanics and Engineering. 372 (2020) - p. 113368 , 2020

Link: https://doi.org/10.1016/..

12

Low rank tensor decompositions for high dimensional data ap..:

Wolf, Alexander Sebastian Johannes Wolf , 2019

Link: https://nbn-resolving.de..

13

The multipole approach for EEG forward modeling using the f..:

Vorwerk, Johannes ; Hanrath, Anne ; Wolters, Carsten H..
NeuroImage. 201 (2019) - p. 116039 , 2019

Link: https://doi.org/10.1016/..

14

Parallel Algorithms for Low Rank Tensor Arithmetic:

, In: Advances in Mechanics and Mathematics; Advances in Mathematical Methods and High Performance Computing,

Grasedyck, Lars ; Löbbert, Christian - p. 271-282 , 2019

Link: https://doi.org/10.1007/..

15

Stable ALS approximation in the TT-format for rank-adaptive..:

Grasedyck, Lars ; Krämer, Sebastian
Numerische Mathematik. 143 (2019) 4 - p. 855-904 , 2019

Link: https://doi.org/10.1007/..