Logo ZeTeM

Zentrum für Technomathematik

ZeTeM > Forschung und Anwendungen > Publikationen

Kontakt Sitemap Impressum [ English | Deutsch ]

Publikationen des Jahres 2019

Zeitschriftenartikel (16)

  1. S. Arridge, P. Maaß, O. Öktem, C. Schönlieb.
    Solving inverse problems using data-driven models.
    Acta Numerica, 28:pp. 1-174, Cambridge University Press, 2019.

    DOI: 10.1017/S0962492919000059

  2. F. Cakoni, H. Haddar, A. Lechleiter.
    On the factorization method for a far field inverse scattering problem in the time domain.
    SIAM Journal on Mathematical Analysis, 51(2):854-872, 2019.

    DOI: 10.1137/18M1214809

  3. J. Clemens, T. Kluth, T. Reineking.
    β - SLAM: Simultaneous Localization an Grid Mapping with Beta Distributions.
    Information Fusion, 52:62-75, Elsevier, 2019.

    DOI: 10.1016/j.inffus.2018.11.005

  4. K. Demertzis, L. Iliadis, I. Bougoudis.
    Gryphon: a semi-supervised anomaly detection system based on one-class evolving spiking neural network.
    Neural Computing and Applications, , Springer Verlag, 2019.

    DOI: 10.1007/s00521-019-04363-x

  5. S. Dittmer, T. Kluth, P. Maaß, D. Otero Baguer.
    Regularization by architecture: A deep prior approach for inverse problems.
    Journal of Mathematical Imaging and Vision, , Springer Verlag, 2019.

    DOI: 10.1007/s10851-019-00923-x
    online unter: http://link.springer.com/article/10.1007/s10851-019-00923-x

  6. M. Eden.
    Homogenization of a Moving Boundary Problem With Prescribed Normal Velocity.
    Erscheint in Advances in Mathematical Sciences and Applications
  7. T. Gerken.
    Dynamic Inverse Wave Problems – Part II: Operator Identification and Applications.
    Erscheint in Inverse Problems

    DOI: 10.1088/1361-6420/ab47f4
    online unter: https://iopscience.iop.org/article/10.1088/1361-6420/ab47f4

  8. T. Gerken, S. Grützner.
    Dynamic Inverse Wave Problems – Part I: Regularity for the Direct Problem.
    Erscheint in Inverse Problems

    DOI: 10.1088/1361-6420/ab47ec
    online unter: https://iopscience.iop.org/article/10.1088/1361-6420/ab47ec

  9. H. Haddar, A. Konschin.
    Factorization Method for Imaging a Local Perturbation in Inhomogeneous Periodic Layers from Far Field Measurements.
    Erscheint in Inverse Problems and Imaging
  10. T. Kluth, B. Jin.
    Enhanced Reconstruction in Magnetic Particle Imaging by Whitening and Randomized SVD Approximation.
    Physics in Medicine and Biology, Article ID 125026 64(12), 2019.

    DOI: 10.1088/1361-6560/ab1a4f

  11. T. Kluth, P. Szwargulski, T. Knopp.
    Towards Accurate Modeling of the Multidimensional Magnetic Particle Imaging Physics.
    New Journal of Physics, Article ID 10303 21, 10 pp., 2019.

    online unter: https://iopscience.iop.org/article/10.1088/1367-2630/ab4938/pdf

  12. A. Konschin, A. Lechleiter.
    Reconstruction of a Local Perturbation in Inhomogeneous Periodic Layers from Partial Near Field Measurements.
    Inverse Problems, 35(11), 32 S., IOPscience, 2019.

    DOI: 10.1088/1361-6420/ab1c66

  13. R. Kuhlmann.
    Learning to Steer Nonlinear Interior-Point Methods.
    EURO Journal on Computational Optimization, 7(4):381-419, 2019.

    DOI: 10.1007/s13675-019-00118-4

  14. K. Schäfer, K. Flaßkamp, J. Fliege, C. Büskens.
    A Combined Homotopy-Optimization Approach to Parameter Identification for Dynamical Systems.
    PAMM, 19, 2019.

    DOI: 10.1002/pamm.201900266

  15. S. Seo, A. Richter, A. Blechschmidt, I. Bougoudis, J. P. Burrows.
    First high-resolution BrO column retrievals from TROPOMI .
    Atmospheric Measurement Techniques, 12:2913-2932, 2019.

    DOI: 10.5194/amt-12-2913-2019

  16. M. Westphal, W. Brannath.
    Evaluation of Multiple Prediction Models: A Novel View on Model Selection and Performance Assessment.
    Statistical Methods in Medical Research, , 2019.

    DOI: 10.1177/0962280219854487

Tagungsbeiträge (12)

  1. J. Behrmann, W. Grathwohl, R. T. Chen, D. Duvenaud, J. Jacobsen.
    Invertible Residual Networks.
    International Conference on Machine Learning (ICML).
    Proceedings of Machine Learning Research, 97:573-582, 2019.

    Long Oral

    online unter: http://proceedings.mlr.press/v97/behrmann19a.html

  2. R. T. Chen, J. Behrmann, D. Duvenaud, J. Jacobsen.
    Residual Flows for Invertible Generative Modeling.
    Neural Information Processing Systems (NeurIPS), 2019.

    Spotlight

    online unter: https://arxiv.org/abs/1906.02735

  3. C. Etmann, S. Lunz, P. Maaß, C. Schönlieb.
    On the Connection Between Adversarial Robustness and Saliency Map Interpretability.
    36th International Conference on Machine Learning, 09.06.-15.06.2019, Los Angeles, USA.
    PMLR 97, 97:1823-1832, 2019.

    online unter: http://proceedings.mlr.press/v97/etmann19a.html

  4. A. Folkers, M. Rick, C. Büskens.
    Controlling an Autonomous Vehicle with Deep Reinforcement Learning.
    Intelligent Vehicles Symposium, 09.06.-12.06.2019, Paris, Frankreich.
    Proceedings of the 30th IEEE Intelligent Vehicles Symposium, S. 2025-2031, 2019.

    Best Student Paper

    DOI: 10.1109/ivs.2019.8814124

  5. J. Jacobsen, J. Behrmann, R. Zemel, M. Bethge.
    Excessive Invariance Causes Adversarial Vulnerability.
    International Conference on Learning Representations (ICLR), 2019.

    online unter: https://openreview.net/forum?id=BkfbpsAcF7

  6. J. Jacobsen, J. Behrmann, N. Carlini, F. Tramer, N. Papernot.
    Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness.
    SafeML Workshop, ICLR, 2019.

    online unter: https://arxiv.org/abs/1903.10484

  7. T. Kluth, B. Hahn, C. Brandt.
    Spatio-temporal concentration reconstruction using motion priors in magnetic particle imaging.
    International Workshop on Magnetic Particle Imaging 2019.
    International Workshop on Magnetic Particle Imaging (IWMPI) Book of Abstracts 2019, S. 23-24, Infinite Science Publishing, 2019.
  8. M. Kurowski, S. Roy, J. Gehrt, R. Damerius, C. Büskens, D. Abel, T. Jeinsch.
    Multi-Vehicle Guidance, Navigation and Control Towards Autonomous Ship Maneuvering in Confined Waters.
    European Control Conference (ECC) 2019, 25.06.-28.06.2019.
    Supervisory control of multilevel discrete-event systems with a bus structure, S. 2559-2564, 2019.

    DOI: 10.23919/ECC.2019.8795726

  9. C. Niebuhr, A. Schmidt.
    Finite element methods for parabolic problems with time-dependent domains - application to a milling simulation.
    ENUMATH 2017.
    Lecture Notes in Computational Science and Engineering, 126:481-489, Springer Verlag, 2019.
  10. M. Rick, J. Clemens, L. Sommer, A. Folkers, K. Schill, C. Büskens.
    Autonomous Driving Based on Nonlinear Model Predictive Control and Multi-Sensor Fusion.
    IFAC Symposium on Intelligent Autonomous Vehicles, 03.07.-05.07.2019.
    Proceedings of the 10th IFAC Symposium on Intelligent Autonomous Vehicles, 52(8):182-187, 2019.

    DOI: 10.1016/j.ifacol.2019.08.068

  11. M. Westphal, W. Brannath.
    Improving Model Selection by Employing the Test Data.
    36th International Conference on Machine Learning, 09.06.-15.06.2019, Los Angeles, USA.
    PMLR 97, S. 6747-6756, 2019.

    online unter: http://proceedings.mlr.press/v97/westphal19a.html

  12. R. Zweigel, J. Gehrt, S. Liu, S. Roy, C. Büskens, M. Kurowski, T. Jeinsch, A. Schubert, M. Gluch, O. Simanski, E. Pairet-Garcia, F. Siemer, D. Abel.
    Optimal Maneuvering and Control of Cooperative Vehicles as Case Study for Maritime Applications within Harbors.
    European Control Conference (ECC) 2019, 25.06.-28.06.2019.
    Supervisory control of multilevel discrete-event systems with a bus structure, S. 3022-3027, 2019.

    DOI: 10.23919/ECC.2019.8796071

Buchkapitel (3)

  1. E. Bänsch, A. Schmidt.
    Free boundary problems in fluids and materials.
    Geometric PDEs, A. Bonito, R. H. Nochetto (Hrsg.), Handbook of Numerical Analysis Vol. 21, S. 65 p., Elsevier, 2019.
  2. M. Knauer, C. Büskens.
    Real-Time Optimal Control Using TransWORHP and WORHP Zen.
    Modeling and Optimization in Space Engineering, G. Fasano, J. D. Pintér (Hrsg.), Springer Optimization and Its Applications, vol 144, S. 211-232, Springer Verlag, 2019.

    DOI: 10.1007/978-3-030-10501-3_9

  3. P. Maaß.
    Deep learning for trivial inverse problems.
    Compressed Sensing and its Applications, H. Boche, R. Calderbank, G. Caire, G. Kutyniok, R. Mathar, P. Petersen (Hrsg.), Applied and Numerical Harmonic Analysis, S. 195-209, Birkhäuser, 2019.

    DOI: 10.1007/978-3-319-73074-5_6

Qualifikationsarbeiten (1)

  1. T. Gerken.
    Dynamic Inverse Problems for Wave Phenomena.
    Dissertationsschrift, Universität Bremen, 2019.

    online unter: https://nbn-resolving.de/urn:nbn:de:gbv:46-00107730-18

Preprints (11)

  1. C. Brandt, M. Hamann, J. Leuschner.
    Regression Models for Ultrasonic Testing of Carbon Fiber Reinforced Polymers.
    Berichte aus der Technomathematik 19–01, Universität Bremen, 2019.
  2. C. Etmann.
    A Closer Look at Double Backpropagation.
    Zur Veröffentlichung eingereicht.

    online unter: https://arxiv.org/abs/1906.06637

  3. E. King.
    2- and 3-Covariant Equiangular Tight Frames.
    Zur Veröffentlichung eingereicht.

    online unter: https://arxiv.org/abs/1901.10612

  4. E. King.
    Constructing Subspace Packings from Other Packings.
    Zur Veröffentlichung eingereicht.

    online unter: https://arxiv.org/abs/1902.07145

  5. T. Kluth, B. Jin.
    Exploiting heuristic parameter choice rules for one-click image reconstruction in magnetic particle imaging.
    Zur Veröffentlichung eingereicht.
  6. T. Kluth, B. Jin.
    L1 data fitting for robust numerical reconstruction in magnetic particle imaging.
    Zur Veröffentlichung eingereicht.
  7. A. Konschin.
    Numerical scheme for electromagnetic scattering on perturbed periodic inhomogeneous media and reconstruction of the perturbation.
    Zur Veröffentlichung eingereicht.
  8. J. Leuschner, M. Schmidt, D. Otero Baguer, P. Maaß.
    The LoDoPaB-CT Dataset: A Benchmark Dataset for Low-Dose CT Reconstruction Methods.
    Zur Veröffentlichung eingereicht.

    online unter: arXiv:1910.01113

  9. R. Reisenhofer, E. King.
    Edge, Ridge, and Blob Detection with Symmetric Molecules.
    Zur Veröffentlichung eingereicht.
  10. L. Siemer, I. Ovsyannikov, J. Rademacher.
    Inhomogenous domain walls in spintronic nanowires.
    Zur Veröffentlichung eingereicht.

    online unter: https://arxiv.org/abs/1907.07470

  11. J. von Schroeder, T. Dickhaus.
    Efficient Calculation of the Joint Distribution of Order Statistics.
    Zur Veröffentlichung eingereicht.

    online unter: https://arxiv.org/abs/1812.09063

Sonstiges (3)

  1. F. Jung, M. Lachmann, W. Bergmann, J. Maldonado, G. Meyer, N. Koop, N. Steenhusen, M. Echim, C. Büskens, K. Schill, R. Frase.
    SmartFarm: Datenbasiert zum optimierten Eigenverbrauch.
    Projektbericht, Abschlussbericht, Bremen, Universität Bremen, Juni 2019.
  2. F. Jung, M. Lachmann, W. Bergmann, J. Maldonado, G. Meyer, N. Koop, N. Steenhusen, M. Echim, C. Büskens, K. Schill, R. Frase.
    SmartFarm: Datenbasiert zum optimierten Eigenverbrauch.
    Zwischenbericht, 02/ 2018, Bremen, Universität Bremen, Januar 2019.
  3. J. von Schroeder, T. Dickhaus, T. Bodnar.
    Reverse Stress Testing in Skew-Elliptical Models.
    Research Report 2019:04, 2019.

    online unter: Mathematical Statistics, Stockholm University