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ZeTeM > Forschung und Anwendungen > Publikationen

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Publikationen des Jahres 2019

Monografien (1)

  1. A. Folkers.
    Steuerung eines autonomen Fahrzeugs durch Deep Reinforcement Learning.
    BestMasters, 75 Seiten, Springer Verlag, 2019.

    DOI: 10.1007/978-3-658-28886-0

Zeitschriftenartikel (31)

  1. A. Ardestani, S. Li, K. Annamalai, B. Lupse, S. Geravandi, A. Dobrowolski, S. Yu, S. Zhu, T. D. Baguley, M. Surakattula, J. Oetjen, L. Hauberg-Lotte, R. Herranz, S. Awal, D. Altenhofen, V. Nguyen-Tran, S. Joseph, P. G. Schultz, A. K. Chatterjee, N. Rogers, M. S. Tremblay, W. Shen, K. Maedler.
    Neratinib protects pancreatic beta cells in diabetes.
    Nature Communications, 10(5015), 2019.

    DOI: 10.1038/s41467-019-12880-5.
    online unter: https://doi.org/10.1038/s41467-019-12880" target="doi">015 | https://doi.org/10.1038/s41467-019-12880

  2. 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

  3. F. Bürgel, K. Kazimierski, A. Lechleiter.
    IPscatt—A MATLAB Toolbox for the Inverse Medium Problem in Scattering.
    ACM Transactions on Mathematical Software, 45(4), 45:1–45:20, 2019.

    DOI: 10.1145/3328525

  4. 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

  5. K. Chandrasekaran, C. Gottschalk, J. Könemann, B. Peis, D. Schmand, A. Wierz.
    Additive stabilizers for unstable graphs.
    Discrete Optimization, 31:56-78, 2019.

    DOI: 10.1016/j.disopt.2018.08.003

  6. 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

  7. 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

  8. P. di Stolfo, A. Rademacher, A. Schröder.
    Dual weighted residual error estimation for the finite cell method.
    Journal of Numerical Mathematics, 27(2):101-122, 2019.
  9. S. Dittmer, E. King, P. Maaß.
    Singular values for ReLU layers.
    IEEE Transactions on Neural Networks and Learning Systems, Article , 2019.

    online unter: https://ieeexplore.ieee.org/document/8891761

  10. I. Dominguez Lacasa, B. Jindra, S. Radosevic, M. Shubbak.
    Paths of technology upgrading in the BRICS economies.
    Research Policy, 48(1):262-280, 2019.

    DOI: 10.1016/j.respol.2018.08.016
    online unter: https://www.sciencedirect.com/science/article/pii/S0048733318302038

  11. M. Eden.
    Homogenization of a Moving Boundary Problem With Prescribed Normal Velocity.
    Advances in Mathematical Sciences and Applications, 28(1):313-341, 2019.
  12. T. Faulwasser, K. Flaßkamp, S. Ober-Blöbaum, K. Worthmann.
    Towards Velocity Turnpikes in Optimal Control of Mechanical Systems.
    IFAC-PapersOnLine, 52(16), 11th IFAC Symposium on Nonlinear Control Systems, Vienna, Austria, 2019.

    DOI: 10.1016/j.ifacol.2019.12.009

  13. K. Flaßkamp, S. Ober-Blöbaum, K. Worthmann.
    Symmetry and Motion Primitives in Model Predictive Control.
    Mathematics of Control, Signals and Systems, :1-31, Springer Verlag, 2019.

    DOI: 10.1007/s00498-019-00246-7

  14. K. Flaßkamp, K. Worthmann, J. Mühlenhoff, C. Greiner-Petter, C. Büskens, J. Oertel, D. Keiner, T. Sattel.
    Towards Optimal Control of Concentric Tube Robots in Stereotactic Neurosurgery.
    Mathematical and Computer Modelling of Dynamical Systems, :560-574, 2019.

    DOI: 10.1080/13873954.2019.1690004

  15. J. Gehrt, R. Zweigel, S. Roy, C. Büskens, M. Kurowski, T. Jeinsch, A. Schubert, M. Gluch, O. Simanski, E. Pairet-Garcia, W. Bruhn, F. Diegel, D. Abel.
    Optimal Maneuvering and Control of Cooperative Vessels within Harbors.
    Journal of Physics, Conference Series, 1357(12019), 2019.

    DOI: 10.1088/1742-6596/1357/1/012019

  16. A. Janz, J. Schramm, M. Echim, F. Schrödel, C. Büskens.
    Model Based Path Optimization for Valet Parking with Trailer.
    IFAC-PapersOnLine, 52(5):85-90, Elsevier, 2019.

    DOI: https://doi.org/10.1016/j.ifacol.2019.09.014

  17. 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

  18. 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

  19. B. Komander, D. Lorenz, L. Vestweber.
    Denoising of image gradients and to- tal generalized variation denoising.
    Journal of Mathematical Imaging and Vision, 61(1):21-39, 2019.

    DOI: 10.1007/s10851-018-0819-8
    online unter: http://arxiv.org/abs/1712.08585

  20. A. Konschin, A. Lechleiter.
    Reconstruction of a Local Perturbation in Inhomogeneous Periodic Layers from Partial Near Field Measurements.
    Inverse Problems, 35(11), 114006, IOPscience, 2019.

    DOI: 10.1088/1361-6420/ab1c66
    online unter: https://iopscience.iop.org/article/10.1088/1361-6420/ab1c66

  21. S. Kraft, M. Moser, C. Büskens, M. Echim.
    Echtzeitfähige Verbrennungssimulation eines Dual-Fuel-Motors für HiL-Anwendung.
    MTZ - Motortechnische Zeitschrift, 11, Springer Verlag, 2019.

    DOI: 10.1007/s35146-019-0115-1

  22. S. Kraft, M. Moser, C. Büskens, M. Echim.
    Real‑time capable combustion simulation of a dual‑fuel engine for hardware‑in‑the‑loop application.
    Heavy-Duty-, On- und Off-Highway-Motoren, :191-206, Springer Verlag, 2019.

    DOI: 10.1007/978-3-658-25889-4_12

  23. 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

  24. D. Lorenz, Q. Tran-Dinh.
    Non-stationary Douglas-Rachford and alternating direction method of multipliers: adaptive stepsizes and convergence.
    Computational Optimization and Applications, 74(1):67-92, 2019.

    DOI: 10.1007/s10589-019-00106-9
    online unter: http://arxiv.org/abs/1801.03765

  25. A. Rademacher.
    Mesh and model adaptivity for frictional contact problems.
    Numerische Mathematik, 142(3):465-523, 2019.
  26. S. Saha, W. Brannath, B. Bornkamp.
    Testing multiple dose combinations in clinical trials.
    Statistical Methods in Medical Research, , 2019.

    DOI: 10.1177/0962280219871969

  27. F. Schöpfer, D. Lorenz.
    Linear convergence of the Randomized Sparse Kaczmarz method.
    Mathematical Programming, 173(1):509-536, 2019.

    DOI: 10.1007/s10107-017-1229-1
    online unter: http://arxiv.org/abs/1610.02889

  28. 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

  29. M. Shubbak.
    Advances in solar photovoltaics: Technology review and patent trends.
    Renewable and Sustainable Energy Reviews, 115, 109383, Elsevier, 2019.

    DOI: 10.1016/j.rser.2019.109383
    online unter: https://www.sciencedirect.com/science/article/pii/S136403211930591X}, author = {Mahmood H. Shubbak

  30. M. Shubbak.
    The technological system of production and innovation: The case of photovoltaic technology.
    Research Policy, 48(4):993-1015, Elsevier, 2019.

    DOI: 10.1016/j.respol.2018.10.003
    online unter: https://www.sciencedirect.com/science/article/pii/S004873331830235X

  31. 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 (22)

  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. J. Behrmann, P. Vicol, K. Wang, R. Grosse, J. Jacobsen.
    On the Invertibility of Invertible Neural Networks.
    NeurIPS workshop on Machine Learning with Guarantees, 2019.

    online unter: https://sites.google.com/view/mlwithguarantees/accepted-papers

  3. A. Berger, M. Knauer, C. Büskens.
    Using Parametric Sensitivity Analysis to Determine Pareto Fronts in Multi-Objective Programming.
    30th European Conference On Operational Research (EURO 2019), 23.06.-26.06.2019.
  4. C. Brauer, Z. Zhao, T. Fingscheidt.
    Learning to de- quantize speech signals by primal-dual networks: an approach for acoustic sensor networks.
    IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

    DOI: 10.1109/ICASSP.2019.8683341

  5. R. T. Chen, J. Behrmann, D. Duvenaud, J. Jacobsen.
    Residual Flows for Invertible Generative Modeling.
    Advances in Neural Information Processing Systems (NeurIPS).
    32, S. 9916--9926, 2019.

    Spotlight

    online unter: https://papers.nips.cc/paper/9183-residual-flows-for-invertible-generative-modeling

  6. T. Czotscher, D. Otero Baguer, F. Vollertsen, I. Piotrowska-Kurczewski, P. Maaß.
    Connection Between Shock Wave Induced Indentations And Hardness By Means Of Neural Networks.
    22nd International Conference on Material Forming (ESAFORM 2019), 08.05.-10.05.2019.
    AIP Conference Proceedings 2113, 100001, Springer Verlag, 2019.

    DOI: 10.1063/1.5112634

  7. 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

  8. 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

  9. 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

  10. 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

  11. M. Klimm, A. Tönnis, D. Schmand.
    The Online Best Reply Algorithm for Resource Allocation Problems.
    12th International Symposium on Algorithmic Game Theory (SAGT 2019).

    DOI: 10.1007/978-3-030-30473-7_14

  12. 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.
  13. 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

  14. D. Lorenz, H. Mahler.
    Orlicz-space regularization for optimal transport and algorithms for quadratic regularization.
    NeurIPS Workshop on Optimal Transport and Machine Learning, Vancouver, Kanada.

    online unter: https://arxiv.org/abs/1909.06082

  15. C. Meerpohl, M. Rick, C. Büskens.
    Free-space Polygon Creation based on Occupancy Grid Maps for Trajectory Optimization Methods.
    10th IFAC Symposium on Intelligent Autonomous Vehicles (IAV 2019), 03.07.-05.07.2019.

    DOI: 10.1016/j.ifacol.2019.08.107

  16. 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.
  17. M. Rick, J. Clemens, L. Sommer, A. Folkers, K. Schill, C. Büskens.
    Autonomous Driving Based on Nonlinear Model Predictive Control and Multi-Sensor Fusion.
    10th IFAC Symposium on Intelligent Autonomous Vehicles (IAV 2019), 03.07.-05.07.2019.

    DOI: 10.1016/j.ifacol.2019.08.068

  18. K. Schäfer, K. Flaßkamp, J. Fliege, C. Büskens.
    A Combined Homotopy-Optimization Approach to Parameter Identification for Dynamical Systems.
    GAMM Annual Meeting of the international Association of Applied Mathematics and Mechanics, 18.02-22.02.2019, Wien, Österreich.
    90, Proc. Appl. Math. Mech., 19, Wiley, 2019.

    DOI: 10.1002/pamm.201900266

  19. D. Schmand, M. Schröder, A. Skopalik.
    Network Investment Games with Wardrop Followers.
    46th International Colloquium on Automata, Languages, and Programming (ICALP 2019).

    DOI: 10.4230/LIPIcs.ICALP.2019.151

  20. S. Schulze, E. King.
    A Frequency‐Uniform and Pitch‐Invariant Time‐Frequency Representation.
    90th GAMM Annual Meeting of the international Association of Applied Mathematics and Mechanics (GAMM), 18.02.-22.02.2019, Wien, Österreich.
    Proc. Appl. Math. Mech., 19(1):e201900374, 2019.

    DOI: 10.1002/pamm.201900374

  21. 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

  22. 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 (7)

  1. S. Bartsch, A. Kolesnikov, M. Echim, C. Büskens.
    Modular Underwater Manipulators for Autonomous Underwater Intervention.
    AI Technology for Underwater Robots, F. Kirchner, S. Straube, D. Kühn, N. Hoyer (Hrsg.), Intelligent Systems, Control and Automation: Science and Engineering, S. 95-103, Springer Verlag, 2019.

    DOI: 10.1007/978-3-030-30683-0_8

  2. I. Dominguez Lacasa, M. Shubbak.
    Technological capabilities in China: Patterns of specialization towards a knowledge intensive economy.
    China's Quest for Innovation: Institutions and Ecosystems, S. Dai, M. Taube (Hrsg.), S. 16, Routledge, London, 2019.

    DOI: 10.4324/9781351019743-5

  3. 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

  4. D. Kumor, A. Rademacher.
    Goal-oriented a posteriori error estimates in nearly incompressible linear elasticity.
    Numerical Mathematics and Advanced Applications, ENUMATH 2017, F. Radu, K. Kumar, I. Berre, J. Nordbotten, I. Pop (Hrsg.), S. 399-406, Springer Verlag, 2019.
  5. A. Luttmann, M. Jahn, A. Schmidt.
    Modeling and Simulation Approaches for the Production of Functional Parts in Micro Scale.
    Progress in Industrial Mathematics at ECMI 2018, Springer Mathematics in Industry Vol. 30, S. 51-58, Springer Verlag, 2019.
  6. 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

  7. A. Schmidt, C. Niebuhr, J. Montalvo Urquizo, M. G. Villarreal-Marroquin.
    Simulation and multi-objective optimization of thermal distortions for milling processes.
    Progress in Industrial Mathematics at ECMI 2018, Springer Mathematics in Industry Vol. 30, S. 421-428, Springer Verlag, 2019.

Qualifikationsarbeiten (7)

  1. J. Behrmann.
    Principles of Neural Network Architecture Design: Invertibility and Domain Knowledge.
    Dissertationsschrift, Universität Bremen, 2019.

    online unter: https://elib.suub.uni-bremen.de/peid/D00108536.html

  2. F. Bürgel.
    Effective and Efficient Reconstruction Schemes for the Inverse Medium Problem in Scattering.
    Dissertationsschrift, Universität Bremen, 2019.
  3. 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

  4. A. Konschin.
    Direkte und inverse elektromagnetische Streuprobleme für lokal gestörte periodische Medien.
    Dissertationsschrift, Universität Bremen, 2019.

    online unter: http://nbn-resolving.de/urn:nbn:de:gbv:46-00107835-13

  5. D. Lantzberg.
    Quantum Frames and Uncertainty Principles arising from Symplectomorphisms.
    Dissertationsschrift, Universität Bremen, 2019.

    online unter: http://nbn-resolving.de/urn:nbn:de:gbv:46-00107143-11

  6. K. Rosin.
    Adaptive Finite Element Methods for Contact Problems embedded in a Fictitious Domain - Simulation and Optimal Control.
    Dissertationsschrift, Technische Universität Dortmund, Dr. Hut Verlag, 2019.
  7. M. Westphal.
    Model Selection and Evaluation in Supervised Machine Learning.
    Dissertationsschrift, Universität Bremen, 2019.

    DOI: 10.26092/elib/16

Preprints (11)

  1. S. Dittmer, P. Maaß.
    A Projectional Ansatz to Reconstruction.
    Zur Veröffentlichung eingereicht.

    online unter: https://arxiv.org/abs/1907.04675

  2. C. Etmann.
    A Closer Look at Double Backpropagation.
    Zur Veröffentlichung eingereicht.

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

  3. C. Etmann, M. Schmidt, J. Behrmann, T. Boskamp, L. Hauberg-Lotte, A. Peter, R. Casadonte, J. Kriegsmann, P. Maaß.
    Deep Relevance Regularization: Interpretable and Robust Tumor Typing of Imaging Mass Spectrometry Data.
    Zur Veröffentlichung eingereicht.

    online unter: https://arxiv.org/abs/1912.05459

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

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

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

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

  6. T. Kluth, B. Jin.
    Exploiting heuristic parameter choice rules for one-click image reconstruction in magnetic particle imaging.
    Zur Veröffentlichung eingereicht.
  7. R. Reisenhofer, E. King.
    Edge, Ridge, and Blob Detection with Symmetric Molecules.
    Zur Veröffentlichung eingereicht.
  8. G. Rigaud.
    3D Compton scattering imaging: study of the spectrum and contour reconstruction.
    Zur Veröffentlichung eingereicht.

    online unter: https://arxiv.org/abs/1908.03066

  9. S. Seo, A. Richter, A. Blechschmidt, I. Bougoudis, J. P. Burrows.
    Spatial distribution of enhanced BrO and its relation to meteorological parameters in Arctic and Antarctic sea ice regions.
    Zur Veröffentlichung eingereicht.

    DOI: 10.5194/acp-2019-996

  10. M. Westphal.
    Simultaneous Inference for Multiple Proportions: A Multivariate Beta-Binomial Model.
    Zur Veröffentlichung eingereicht.

    online unter: https://arxiv.org/abs/1911.00098

  11. M. Westphal, A. Zapf, W. Brannath.
    A multiple testing framework for diagnostic accuracy studies with co-primary endpoints.
    Zur Veröffentlichung eingereicht.

    online unter: https://arxiv.org/abs/1911.02982

Sonstiges (6)

  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. A. Folkers.
    Steuerung eines autonomen Fahrzeugs durch Deep Reinforcement Learning.
    BestMasters, 75 pages, Springer Verlag, Universität Bremen, 2019.
  3. 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.
  4. 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.
  5. D. Kumor, A. Rademacher.
    Goal oriented a posteriori error estimators for problems with modified discrete formulations based on the dual weighted residual method.
    Projektbericht, Ergebnisberichte Angewandte Mathematik, Fakultät für Mathematik, Technische Universität Dortmund, 596, 2019.
  6. 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