Publikationen der AG Technomathematik

Zeitschriftenartikel (8)

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.
   Inverse Problems, 40 045021 40(4), IOPscience, 2024.

   DOI: 10.1088/1361-6420/ad2aaa
   online unter: https://www.x-mol.net/paper/article/1682514725633245184

2. A. Denker, Z. Kereta, I. Singh, T. Freudenberg, T. Kluth, B. Maass, S. Arridge.
   Data-driven approaches for electrical impedance tomography image segmentation from partial boundary data.
   Applied Mathematics for Modern Challenges, 2(2):119-139, 2024.

   DOI: 10.3934/ammc.2024005

3. P. Jansen, J. Le Clerc Arrastia, D. Otero Baguer, M. Schmidt, J. Landsberg, J. Wenzel, M. Emberger, D. Schadendorf, E. Hadaschik, P. Maaß, K. G. Griewank.
   Deep learning based histological classification of adnex tumors.
   European Journal of Cancer, 113431 196, 2024.

   DOI: 10.1016/j.ejca.2023.113431

4. J. G. Maaß, R. Herdt, L. Kinzel, M. Walther, H. Fröhlich, T. Schubert, C. Schaaf, P. Maaß.
   Enhancing the analysis of murine neonatal ultrasonic vocalizations: Development, evaluation, and application of different mathematical models.
   The Journal of the Acoustical Society of America, 156(4):2448-2466, 2024.

   DOI: 10.1121/10.0030473

5. M. Beckmann, N. Heilenkötter.
   Equivariant Neural Networks for Indirect Measurements.
   SIAM Journal on Mathematics of Data Science, 6(3), 2024.

   DOI: 10.1137/23M1582862
   online unter: https://epubs.siam.org/doi/10.1137/23M1582862

6. D. Erzmann, S. Dittmer.
   Equivariant Neural Operators for gradient-Consistent Topology Optimization .
   Journal of Computational Design and Engineering, 11(3):91-100, 2024.

   DOI: 10.1093/jcde/qwae039

7. M. Beckmann, A. Bhandari, M. Iske.
   Fourier-Domain Inversion for the Modulo Radon Transform.
   IEEE Transactions on Computational Imaging, 10, 2024.

   online unter: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10499837

8. O. Dorn, Y. Zhong, D. Lesselier.
   New trends in electromagnetic inverse problems.
   Inverse Problems, 40 120201 40(12), IOPscience, 2024.

   DOI: 10.1088/1361-6420/ad98bd

Tagungsbeiträge (3)

1. P. Fernsel, Z. Kereta, A. Denker.
   Convergence Properties of Score-Based Models using Graduated Optimisation for Linear Inverse Problems.
   2024 IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP), 22.09.-25.09.2024, London, Großbritannien.
   IEEE, S. 1-6, 2024.

   DOI: 10.1109/MLSP58920.2024.10734770

2. D. Hauser, M. Beckmann, G. Koliander, H. S. Stiehl.
   On Image Processing and Pattern Recognition for Thermograms of Watermarks in Manuscripts - A First Proof-of-Concept.
   18th International Conference on Document Analysis and Recognition (ICDAR), 30.08.-04.09.2024.
   

   DOI: 10.1007/978-3-031-70543-4_6

3. R. Herdt, P. Maaß.
   Visualize and Paint GAN Activations.
   2024 IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP), 22.09.-25.09.2024, London, Großbritannien.
   IEEE, 2024.

   DOI: 10.1109/MLSP58920.2024.10734757

Qualifikationsarbeiten (4)

1. D. . Dobrovolskij.
   3D image analysis and microstructure models for simulation of materials properties.
   Dissertationsschrift, Universität Bremen, 2024.

   DOI: 10.26092/elib/3052

2. D. Erzmann.
   Equivariant deep learning for 3D topology optimization.
   Dissertationsschrift, Universität Bremen, 2024.

   DOI: 10.26092/elib/3439

3. A. Denker.
   Invertible Neural Networks and Normalizing Flows for Image Reconstruction.
   Dissertationsschrift, Universität Bremen, 2024.

   DOI: 10.26092/elib/2921

4. L. Kinzel.
   Unsupervised Deep Machine Learning Methods to Discriminate Icequakes in Seismological Data from Neumayer Station, Antarctica.
   Dissertationsschrift, Universität Bremen, 2024.

   DOI: 10.26092/elib/3013

Preprints (2)

1. C. Arndt, J. Nickel.
   Invertible ResNets for inverse imaging problems: Competitive performance with provable regularization properties.
   Zur Veröffentlichung eingereicht.

   online unter: https://arxiv.org/abs/2409.13482

2. R. Herdt, M. Schmidt, D. Otero Baguer, P. Maaß.
   Smooth Deep Saliency.
   Zur Veröffentlichung eingereicht.

   online unter: https://arxiv.org/abs/2404.02282