Dr. Sören Dittmer

Projekte

1. Design-KIT: Künstliche Intelligenz in der mechanischen Bauteilentwicklung; TP: Deep Learning zur Geometrieerzeugung von mechanischen Bauteilen (01.10.2020 - 31.03.2022)
2. Magnetic Particle Imaging (seit 01.03.2016)

Veranstaltungen (Auswahl)vollständige Liste

1. Mathematical Methods in Machine Learning (Wintersemester 2023/2024)
2. Mathematical Foundations of AI (Sommersemester 2023)
3. Mathematical Foundations of Deep Learning (Wintersemester 2022/2023)
4. Mathematical Foundations of AI (Wintersemester 2022/2023)
5. Mathematical Foundations of AI (Sommersemester 2022)

Abschlussarbeiten (Auswahl)vollständige Liste

1. Contrasting and motivating augmented contrastive learning (Jule Pätzold)
2. Inverse Problems Learning – data specific regularizations using projections (Julius Arkenberg)
3. Unsupervised Denoising von Magnetic-Particle-Imaging-Messungen durch Neuronale Netze (Nikolas Dreverhoff)
4. Differentiable architecture search - Fractional Kernel sizes in convolutional neural networks (Daniel Klosa)

Publikationen (Auswahl)vollständige Liste

1. 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

2. 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

3. D. Erzmann, S. Dittmer, H. Harms, P. Maaß.
   DL4TO: A Deep Learning Library for Sample-Efficient Topology Optimization.
   Lecture Notes in Computer Science, Geometric Science of Information. GSI 2023 14071, Springer Verlag, 2023.

   DOI: 10.1007/978-3-031-38271-0_54

4. C. Arndt, A. Denker, S. Dittmer, J. Leuschner, J. Nickel, M. Schmidt.
   Model-based deep learning approaches to the Helsinki Tomography Challenge 2022.
   Applied Mathematics for Modern Challenges, 1(2), 2023.

   DOI: 10.3934/ammc.2023007

5. S. Dittmer, M. Roberts, J. Gilbey, A. Biguri, .. AIX-COVNET Collaboration, J. Preller, J. H. F. Rudd, J. A. D. Aston, C. Schönlieb.
   Navigating the development challenges in creating complex data systems.
   nature machine intelligence, 5:681-686, Springer Verlag, 2023.

   DOI: 10.1038/s42256-023-00665-x
   online unter: https://www.nature.com/articles/s42256-023-00665-x#citeas