Publikationen von Nick Heilenkötter

Zeitschriftenartikel (3)

1. 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

2. 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.
   Inverse Problems, 39(12), IOPscience, 2023.

   DOI: 10.1088/1361-6420/ad0660
   online unter: https://iopscience.iop.org/article/10.1088/1361-6420/ad0660

3. 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.
   MDPI Journal of Imaging, 71 7(4), Meisenbach Verlag, Bamberg, 2021.

   DOI: 10.3390/jimaging7040071

Preprints (2)

1. 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

2. M. Beckmann, N. Heilenkötter.
   Equivariant Neural Networks for Indirect Measurements.
   Zur Veröffentlichung eingereicht.

   online unter: https://arxiv.org/abs/2306.16506