Nick Heilenkötter

Zeitschriftenartikel

D. Nganyu Tanyu, J. Ning, T. Freudenberg, N. Heilenkötter, A. Rademacher, U. Iben, P. Maaß.
Deep learning methods for partial differential equations and related parameter identification problems.
Inverse Problems, 39(10), 2023.
DOI: 10.1088/1361-6420/ace9d4

J. Le’Clerc Arrastia, N. Heilenkötter, D. Otero Baguer, L. Hauberg-Lotte, T. Boskamp, S. Hetzer, N. Duschner, J. Schaller, P. Maass.
Deeply Supervised UNet for Semantic Segmentation to Assist Dermatopathological Assessment of Basal Cell Carcinoma. 
Journal of Imaging. 2021; 7(4):71.
DOI: 10.3390/jimaging7040071

Preprints

C. Arndt, A. Denker, S. Dittmer, N. Heilenkötter, M. Iske, T. Kluth, P. Maaß, J. Nickel.
Invertible residual networks in the context of regularization theory for linear inverse problems.
Zur Veröffentlichung eingereicht.
online unter: https://arxiv.org/abs/2306.01335

C. Arndt, S. Dittmer, N. Heilenkötter, M. Iske, T. Kluth, J. Nickel.
Bayesian view on the training of invertible residual networks for solving linear inverse problems.
Zur Veröffentlichung eingereicht.
online unter: https://www.x-mol.net/paper/article/1682514725633245184

Derick Nganyu Tanyu, Jianfeng Ning, Tom Freudenberg, Nick Heilenkötter, Andreas Rademacher, Uwe Iben, Peter Maass
Deep Learning Methods for Partial Differential Equations and Related Parameter Identification Problems
online unter: arxiv.org/abs/2212.03130
DOI:  10.48550/arXiv.2212.03130