Publications
L. Fischer, A. Ramdas.
Sequential Monte Carlo testing by betting.
Jorunal of the Royal Statistical Society, Series B: Statistical Methodology, 2025
DOI: 10.1093/jrsssb/qkaf014
R. Luschei, W. Brannath.
The effect of estimating prevalences on the population-wise error rate.
Statistical Methods in Medical Research, 2025.
DOI: 10.1177/09622802241307237
C. Arndt, S. Dittmer, N. Heilenk?tter, 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
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
M. Beckmann, N. Heilenk?tter.
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
M. Beckmann, J. Nickel.
Optimized filter functions for filtered back projection reconstructions. Inverse Problems and Imaging.
Inverse Problems and Imaging, , 2024.
DOI: 10.48550/arXiv.2408.06471
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
S. Dittmer, C. Sch?nlieb, P. Maa?.
Ground Truth Free Denoising by Optimal Transport.
Numerical Algebra, Control, and Optimization, 14(1) p. 34-58, 2024
DOI: 10.3934/naco.2022017
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
M. Eden, T. Freudenberg.
Effective Heat Transfer Between a Porous Medium and a Fluid Layer: Homogenization and Simulation.
Multiscale Modeling & Simulation: A SIAM interdisciplinary journal, 22(2):752-783, 2024.
DOI: 10.1137/22M154179
S. Feldmann, T. Schürenberg.
On the Min-Max Star Partitioning Number.
Statistical Methods in Medical ResearchQ. Bramas, A. Casteigts, A. Meeks (eds) Algorithmics of Wireless Networks. ALGOWIN 2024. Lecture Notes in Computer Science, Vol 15026, Springer, Cham.20 24
DOI: 10.1007/978-3-031-74580-5_5
L. Fischer, M. Bofill Roig, W. Brannath.
An exhaustive ADDIS principle for online FWER control.
Biometrical Journal 66(3): 2300237, 2024.
DOI: 10.1002/bimj.202300237
L. Fischer, M. Bofill Roig, W. Brannath.
The online closure principle.
Ann. Statist. 52 (2) 817 - 841, April 2024.
DOI: 10.1214/24-AOS2370
T. Freudenberg, M. Eden.
Homogenization and simulation of heat transfer through a thin grain layer.
Networks and Heterogeneous Media, 19(2):569-596, 2024.
DOI: 10.3934/nhm.2024025
R. Herdt, L. Kinzel, J. Maa?, M. Walther, H. Fr?hlich, T. Schubert, P. Maass, C.P. Schaaf.
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)
DOI: 10.1121/10.0030473
P. Jansen, J. Le Clerc Arrastia, D. Otero Baguer, M. Schmidt, J. Landsberg, J. Wenzel, M. Emberger, D. Schadendorf, E. Hadaschik, P. Maa?, 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
L. Kinzel, T. Fromm, V. Schlindwein, P. Maass.
Unsupervised Deep Feature Learning for Icequake Discrimination at Neumayer Station, Antarctica
Seismological Research Letters, 95(3):1834-1848
DOI: 10.1785/0220230078
D. Ochieng.
Multiple testing of interval composite null hypotheses using randomized p-values.
Statistical Papers, 65(8), 5055-5076, 2024.
DOI: 10.1007/s00362-024-01591-9
F. Preusse, A. Vesely and T. Dickhaus.
Confidence bounds for the true discovery proportion based on the exact distribution of the number of rejections.
Annals of the Institute of Statistical Mathematics, 2024.
DOI: 10.1007/s10463-024-00920-x
T. Schürenberg, S. Feldmann.
On the Min-Max Star Partitioning Number.
Algorithmics of Wireless Networks, 15026:61-75, Springer Verlag, 2024.
DOI: 10.1007/978-3-031-74580-5_5
online unter: https://link.springer.com/chapter/10.1007/978-3-031-74580-5_5
I. Singh, A. Denker, R. Barbano, Z. Kereta, B. Jin, K. Thielemans, P. Maass, S. Arridge.
Score-Based Generative Models for PET Image Reconstruction.
Journal of Machine Learning for Biemedical Imaging, Vol. 2, pp. 547-585
DOI: 10.59275/j.melba.2024-5d51
F. Altekrüger, A. Denker, P. Hagemann, J. Hertrich, P. Maass, G. Steidl.
PatchNR: Learning from Very Few Images by Patch Normalizing Flow Regularization.
Inverse Problems 39 (6), 064006, 2023.
J. Antorán, R. Barbano, J. Leuschner, J.M. Hernández-Lobato, B. Jin
Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior.
Transactions on Machine Learning Research, ISSN: 2835-8856, 2023.
(https://openreview.net/forum?id=FWyabz82fH)
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, 2023.
C. Arndt, A. Denker, S. Dittmer, N. Heilenkoetter, M. Iske, T. Kluth, P. Maass and J. Nickel.
Invertible residual networks in the context of regularization theory for linear inverse problems,
Inverse Problems 39 125018, 2023.
DOI: 10.1088/1361-6420/ad0660.
C. W. Bang and V. Didelez.
Do we become wiser with time? On causal equivalence with tiered background knowledge.
Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, pages 119 – 129. PMLR, 2023.
E. Dierkes, C. Offen, S. Ober-Bl?baum, and K. Fla?kamp
Hamiltonian neural networks with automatic symmetry detection.
Chaos: An Interdisciplinary Journal of Nonlinear Science, 33(6), 2023
D. Erzmann, S. Dittmer, H. Harms, P. Maa?.
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
S. Dittmer, M. Roberts, J. Gilbey, A. Biguri, .. AIX-COVNET Collaboration, J. Preller, J. H. F. Rudd, J. A. D. Aston, C. Sch?nlieb.
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
J. G?deke, G. Rigaud.
Imaging based on Compton scattering: model uncertainty and data-driven reconstruction methods.
Inverse Problems, 39(3), 2023.
DOI: 10.1088/1361-6420/acb2ed
D. Hinse, M. Thode, A. Rademacher, K. Pantke, C. Spura.
Numerical identification of position-dependent friction coefficients from measured displacement data in a bolt-nut connection.
Results in Engineering, 19: 101214, 2023.
G. Klaila, V. Vutov, A. Stefanou.
Supervised topological data analysis for MALDI mass spectrometry imaging applications
BMC bioinformatics, 2023, 24. Jg., Nr. 1, S. 279.
R. Luschei and W. Brannath .
The effect of estimating prevalences on the population-wise error rate.
ArXiv: 2304.09988, 2023
https://doi.org/10.48550/arXiv.2304.09988
H. Mohn, D. Kreyling, I. Wohltmann, R. Lehmann, P. Maass, M. Rex.
Neural representation of the stratospheric ozone chemistry. Environmental Data Science.
Cambridge University Press, 2023
DOI: 10.1017/eds.2023.35
D. Nganyu Tanyu, J. Ning, T. Freudenberg, N. Heilenk?tter, A. Rademacher, U. Iben, P. Maa?.
Deep learning methods for partial differential equations and related parameter identification problems.
Inverse Problems, 2023.
DOI: 10.1088/1361-6420/ace9d4
D. Ochieng, A.-T. Hoang, and T. Dickhaus .
Multiple testing of composite null hypotheses for discrete data using randomized p-values
Biometrical Journal, 2023.
T. Shadbahr, M. Roberts, J. Stanczuk, J. Gilbey, P. Teare, S. Dittmer, M. Thorpe, R. V. Torne, E. Sala, P. Lio, M. Patel, .. AIX-COVNET Collaboration, J. H. F. Rudd, T. Mirtti, A. Rannikko, J. A. D. Aston, J. Tang, C. Sch?nlieb.
The impact of imputation quality on machine learning classifiers for datasets with missing values.
Communication medicine, 3, Springer Verlag, 2023.
DOI: 10.1038/s43856-023-00356-z
online unter: https://www.nature.com/articles/s43856-023-00356-z#citeas
M. Steinherr Zazo, J. D. M. Rademacher
Bifurcation Control for a Ship Maneuvering Model with Nonsmooth Nonlinearities
SIAM Journal on Control and Optimization, 61(1), 2023.
DOI: 10.1137/21M1417259
V. Vutov, T. Dickhaus.
Multiple multi-sample testing under arbitrary covariance dependency
Statistics in Medicine, 42(17), 2023
DOI: 10.1002/sim.9761
Wichmann, M., Eden, M, Zvegincev, D. et al.
Modeling the wetting behavior of grinding wheels.
Int J Adv Manuf Technol 128, 1741-1747 (2023).
https://doi.org/10.1007/s00170-023-12002-y
F. Wiesener, B. Bergmann, M. Wichmann, M. Eden, T. Freudenberg, A. Schmidt.
Modeling of heat transfer in tool grinding for multiscale simulations.
Procedia CIRP, 2023.
H. Albers, T. Kluth, T. Knopp.
Simulating magnetization dynamics of large ensembles of single domain nanoparticles: Numerical study of Brown/Néel dynamics and parameter identification problems in magnetic particle imaging.
Journal of Magnetism and Magnetic Materials, 541, 168508, Elsevier, 2022.
DOI: 10.1016/j.jmmm.2021.168508
online unter: https://www.sciencedirect.com/science/article/abs/pii/S0304885321007678
H. Albers, T. Knopp, M. M?ddel, M. Boberg, T. Kluth.
Modeling the magnetization dynamics for large ensembles of immobilized magnetic nanoparticles in multi-dimensional magnetic particle imaging.
Journal of Magnetism and Magnetic Materials, 543, 168534, Elsevier, 2022.
DOI: 10.1016/j.jmmm.2021.168534
C. Arndt, A. Denker, J. Nickel, J. Leuschner, M. Schmidt, G. Rigaud.
In Focus - hybrid deep learning approaches to the HDC2021 challenge.
Inverse Problems and Imaging, , 2022.
DOI: 10.3934/ipi.2022061
R. Barbano, J. Leuschner, M. Schmidt, A. Denker, A. Hauptmann, P. Maa?, B. Jin.
An Educated Warm Start For Deep Image Prior-based Micro CT Reconstruction.
IEEE Transactions on Computational Imaging, 8:1210-1222, 2022.
DOI: 10.1109/TCI.2022.3233188
A.T. Hoang, T. Dickhaus.
Combining independent p-values in replicability analysis: a comparative study.
Journal of Statistical Computation ans Simulation, 2022.
DOI: 10.1080/00949655.2021.2022678
I. Mykhailiuk, K. Sch?fer, C. Büskens.
Parametric stability score and its application in optimal control.
IFAC-PapersOnLine, 55(16):172-177, Elsevier, 2022.
DOI: 10.1016/j.ifacol.2022.09.019
P. Rink, W. Brannath .
Post-Selection Confidence Bounds for Prediction Performance.
Preprint arXiv:2210.13206. 2022.
Submitted to Machine Learning (Springer).
V. Vutov, T. Dickhaus.
Multiple two-sample testing under arbitrary covariance dependency with an application in imaging mass spectrometry.
Biometrical ournal, 65(2),2022
DOI: 10.1002/bimj.202100328
M. Westphal, A. Zapf, W. Brannath.
A multiple testing framework for diagnostic accuracy studies with co-primary endpoints.
Statistics in medicine, 41(5), p 891-909, 2022
online at: https://arxiv.org/abs/1911.02982
S. Arridge, P. Fernsel, A. Hauptmann.
Joint reconstruction and low-rank decomposition for dynamic inverse problems..
Inverse Problems & Imaging, 2021
DOI: 10.3934/ipi.2021059
A. Denker, M. Schmidt, J. Leuschner, P. Maa?.
Conditional Invertible Neural Networks for Medical Imaging .
MDPI Journal of Imaging, Inverse Problems and Imaging 7(11), 243 S., 2021.
DOI: 10.3390/jimaging7110243
E. Dierkes and K. Fla?kamp .
Learning Hamiltonian Systems considering System Symmetries in Neural Networks.
IFAC-PapersOnLine, 54(19):210–216. 2021.
E. Dierkes, C. Meerpohl, K. Fla?kamp and C. Büskens (2021).
Estimation and Mapping of System-Surface Interaction by Combining Nonlinear Optimization and Machine Learning.
IFAC-PapersOnLine, 54(14):138-143. 2021.
E. Dierkes, F. Jung and C. Büskens .
Data-based models of drive technology for automation in automotive production.
Proc. Appl. Math. Mech., 20(1). 2021.
E. Dierkes and K. Fla?kamp (2021).
Learning Mechanical Systems by Hamiltonian Neural Networks.
In Proc. Appl. Math. Mech., 21(1), 2021.
S. Dittmer, T. Kluth, M. Henriksen, P. Maa?.
Deep image prior for 3D magnetic particle imaging: A quantitative comparison of regularization techniques on Open MPI dataset.
International Journal on Magnetic Particle Imaging, 7(1), 2021.
online unter: https://journal.iwmpi.org/index.php/iwmpi/article/view/148
P. Fernsel .
Spatially Coherent Clustering Based on Orthogonal Nonnegative Matrix Factorization..
J. Imaging 2021, 7, 194, 2021.
DOI: 10.3390/jimaging7100194
A.T. Hoang, T. Dickhaus.
On the usage of randomized p-values in the Schweder-Spj?tvoll estimator .
Annals of the Institute of Statistical Mathematics, 2021.
A.T. Hoang, T. Dickhaus.
Randomized p-values for multiple testing and their application
in replicability analysis .
Biometrical Journal, 2021;1-16.
DOI: 10.1002/bimj.202000155
A. Konschin.
Electromagnetic wave scattering from locally perturbed periodic inhomogeneous layers.
Mathematical Methods in the Applied Sciences, 44(18), p, 14126-14147, 2021
DOI: 10.1002/mma.7680
A. Konschin.
Numerical scheme for electromagnetic scattering on perturbed periodic inhomogeneous media and reconstruction of the perturbation.
SIAM Journal on Scientific Computing, 43(3), 2021
DOI: 10.1137/20M1350716
J. Le'Clerc Arrastia, N. Heilenk?tter, D. Otero Baguer, L. Hauberg-Lotte, T. Boskamp, S. Hetzer, N. Duschner, J. Schaller, P. Maa?.
Deeply Supervised UNet for Semantic Segmentation to Assist Dermatopathological Assessment of Basal Cell Carcinoma..
J. Imaging 2021, 7(4), 71, 2021
DOI: 10.3390/jimaging7040071
J. Leuschner, M. Schmidt, D. Otero Baguer, P. Maa?.
LoDoPaB-CT, a benchmark dataset for low-dose computed tomography reconstruction.
Scientific Data, 8(109), 2021.
DOI: 10.1038/s41597-021-00893-z
J. Leuschner, M. Schmidt, P. Ganguly, V. Andriiashen, S. Coban, A. Denker, D. Bauer, A. Hadjifaradji, K. Batenburg, B. Maass, M. von Eijnatten.
Quantitative Comparison of Deep Learning-Based Image Reconstruction Methods for Low-Dose and Sparse-Angle CT Applications.
MDPI Journal of Imaging, 7(3), 44 S., 2021.
DOI: 10.3390/jimaging7030044 online unter: https://www.mdpi.com/2313-433X/7/3/44
J.D.M. Rademacher, Lars Siemer
Domain wall motion in axially symmetric spintronic nanowires
SIAM J. Appl. Dyn. Syst., 20(4), 2204–2235 (2021)
DOI: 10.1137/20M1382696
S. Schulze, J. Leuschner, E. King.
Blind source separation in polyphonic music recordings using deep neural networks trained via policy gradients..
Signals 2021, 2(4), 637-661, 2021
DOI: 10.3390/signals2040039
S. Schulze, E. King.
Sparse Pursuit and Dictionary Learning for Blind Source Separation in Polyphonic Music Recordings.
J AUDIO SPEECH MUSIC PROC.2021, 6 (2021).
DOI: 10.1186/s13636-020-00190-4
H. Albers, T. Kluth, T. Knopp.
MNPDynamics: A computational toolbox for simulating magnetic moment behavior of ensembles of nanoparticles.
Int. J. Mag. Part. Imag. 6(2), Suppl. 1, 2020,
Article ID: 2009020,
DOI: 10.18416/IJMPI.2020.2009020 (Conference Proceedings)
M. Beckmann, P. Maass and J. Nickel.
Error analysis for filtered back projection reconstruc-tions in Besov spaces.
Inverse Problems, 37(1), IOP Science. 2020.
DOI: https://doi.org/10.1088/1361-6420/aba5ee.
S. Dittmer, T. Kluth, D. Otero Baguer, B. Maass.
A Deep Prior Approach to Magnetic Particle Imaging.
Machine Learning for Medical Image Reconstruction 2020.
Springer International Publishing, F. Deeba, P. Johnson, T. Würfl, J. C. Ye (Hrsg.), S. 113-122, 2020.
DOI: 10.1007/978-3-030-61598-7_11
T. Gerken, S. Grützner.
Dynamic Inverse Wave Problems – Part I: Regularity for the Direct Problem.
Inverse Problems, 36(2), IOPscience, 2020.
DOI: 10.1088/1361-6420/ab47ec
online at: https://iopscience.iop.org/article/10.1088/1361-6420/ab47ec
T. Gerken.
Dynamic Inverse Wave Problems – Part II: Operator Identification and Applications.
Inverse Problems, 36(2), IOPscience, 2020.
DOI: 10.1088/1361-6420/ab47f4
online at: https://iopscience.iop.org/article/10.1088/1361-6420/ab47f4
H. Haddar, A. Konschin.
Factorization Method for Imaging a Local Perturbation in Inhomogeneous Periodic Layers from Far Field Measurements.
Inverse Problems and Imaging, 14(1):133-152, 2020.
DOI: 10.3934/ipi.2019067
online at: https://www.aimsciences.org/article/doi/10.3934/ipi.2019067
T. Kluth, C. Bathke, M. Jiang, P. Maa?.
Joint super-resolution image reconstruction and parameter identification in imaging operator: Analysis of bilinear operator equations, numerical solution, and application to magnetic particle imaging.
Inverse Problems, 36 124006, 2020
DOI: 10.1088/1361-6420/abc2fe
D. Otero Baguer, J. Leuschner, M. Schmidt.
Computed Tomography Reconstruction Using Deep Image Prior and Learned Reconstruction Methods.
Inverse Problems, 36(9), IOPscience, 2020.
DOI: https://doi.org/10.1088/1361-6420/aba415
L. Siemer, I. Ovsyannikov, J. Rademacher.
Inhomogeneous domain walls in spintronic nanowires.
Nonlinearity, 2905 33(6), IOPscience, 2020.
DOI: 10.1088/1361-6544/ab6f4e
J. von Schroeder, T. Dickhaus.
Efficient Calculation of the Joint Distribution of Order Statistics.
Computational Statistics & Data Analysis, 144, Elsevier, 2020.
online at: https://doi.org/10.1016/j.csda.2019.106899
S. Seo, A. Richter, A.-M. Blechschmidt, I. Bougoudis, J.P. Burrows.
Spatial distribution of enhanced BrO and its relation to meteorological parameters in Arctic and Antarctic sea ice regions.
Atmospheric Chemistry and Physics, 20, 12285-12312, 2020
DOI: 10.5194/acp-20-12285-2020
I. Bougoudis, A. Blechschmidt, A. Richter, S. Seo, J. P. Burrows, N. Theys, A. Rinke.
Long-term Time-series of Arctic Tropospheric BrO derived from UV-VIS Satellite Remote Sensing and its Relation to First Year Sea Ice.
Atmospheric Chemistry and Physics, 20, 11869-11892, 2020
DOI: 10.5194/acp-20-11869-2020
M. Steinherr Zazo, J. Rademacher.
Lyapunov coefficients for Hopf bifurcations in systems with piecewise smooth nonlinearity.
SIAM Journal on Applied Dynamical Systems, 19(4):2847-2886, 2020
DOI: 10.1137/20M1343129
S. Dittmer, T. Kluth, P. Maa?, D. Otero Baguer.
Regularization by architecture: A deep prior approach for inverse problems.
Journal of Mathematical Imaging and Vision, :456-470, Springer Verlag, 2020.
DOI: 10.1007/s10851-019-00923-x
online at: http://link.springer.com/article/10.1007/s10851-019-00923-x
M. Lachmann, J. Maldonado, W. Bergmann, F. Jung, M. Weber, C. Büskens.
Self-Learning Data-Based Models as Basis of a Universally Applicable Energy Management System.
Energies 2020, 13(8), 2084, 2020.
DOI: 10.3390/en13082084
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
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.
M. Westphal, W. Brannath.
Evaluation of Multiple Prediction Models: A Novel View on Model Selection and Performance Assessment.
Statistical Methods in Medical Research, , 2019.
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.
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
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.
S. Dittmer, E. King, P. Maa?.
Singular values for ReLU layers.
IEEE Transactions on Neural Networks and Learning Systems, Article , 2019.
online at: https://ieeexplore.ieee.org/document/8891761
S. Saha, W. Brannath, B. Bornkamp.
Testing multiple dose combinations in clinical trials.
Statistical Methods in Medical Research, , 2019.
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 at: https://iopscience.iop.org/article/10.1088/1367-2630/ab4938/pdf
P. Fernsel, P. Maa?.
A Survey on Surrogate Approaches to Non-negative Matrix Factorization.
Vietnam Journal of Mathematics, 46(4):987-1021, Springer Verlag, 2018.
DOI: 10.1007/s10013-018-0315-x
S. Saha, W. Brannath.
Comparison of different approaches for dose response analysis.
Biometrical Journal, 61(1):83-100, WILEY-VCH, 2018.
J. Behrmann, C. Etmann, T. Boskamp, R. Casadonte, J. Kriegsmann, P. Maa?.
Deep Learning for Tumor Classification in Imaging Mass Spectrometry.
Bioinformatics, 34(7):1215-1223, Oxford University Press, 2018.
DOI: 10.1093/bioinformatics/btx724
I. Bougoudis, K. Demertzis, L. Iliadis, V. . Anezakis, A. Papaleonidas.
FuSSFFra, a fuzzy semi-supervised forecasting framework: the case of the air pollution in Athens.
Neural Computing and Applications, 7, 2018.
T. Kluth.
Mathematical models for magnetic particle imaging.
Inverse Problems, Article ID 083001 34(8), 2018.
T. Kluth, B. Jin, G. Li.
On the Degree of Ill-Posedness of Multi-Dimensional Magnetic Particle Imaging.
Inverse Problems, Article ID 095006 34(9), 2018.
J. Leuschner, M. Schmidt, P. Fernsel, D. Lachmund, T. Boskamp, P. Maa?.
Supervised Non-negative Matrix Factorization Methods for MALDI Imaging Applications.
Bioinformatics, bty909 , 2018.
DOI: 10.1093/bioinformatics/bty909
C. Bathke, T. Kluth, C. Brandt, P. Maa?.
Improved image reconstruction in magnetic particle imaging using structural a priori information.
International Journal on Magnetic Particle Imaging, Article ID 1703015, 3(1), 10 pages, 2017.
DOI: 10.18416/ijmpi.2017.1703015
T. Kluth, P. Maa?.
Model uncertainty in magnetic particle imaging: Nonlinear problem formulation and model-based sparse reconstruction.
International Journal on Magnetic Particle Imaging, Article ID 1707004 3(2), 10 pages, 2017.
DOI: 10.18416/ijmpi.2017.1707004
T. Gerken, A. Lechleiter.
Reconstruction of a Time-dependent Potential from Wave Measurements.
Inverse Problems, Article ID 094001 33(9), IOPscience, 2017.
(Highlight Paper)
DOI: 10.1088/1361-6420/aa7e07
online at: http://iopscience.iop.org/article/10.1088/1361-6420/aa7e07
E. Dierkes, L. Kappertz, V. Solovievskyi, A. Hackenberg, C. Büskens.
Optimized Self-Consumption of Renewable Energies With Forecast-Based Energy Management for Agricultural Farms.
GAMM 94th Annual Meeting of the International Association of Applied Mathematics and Mechanics, 01.12.2024, Magdeburg, Deutschland.
DOI: 10.1002/pamm.202400068
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, Gro?britannien.
IEEE, S. 1-6, 2024.
DOI: 10.1109/MLSP58920.2024.10734770
R. Herdt, M. Schmidt, D. Otero Baguer, J. Le Clerc Arrastia, P. Maa?.
How GAN Generators can Invert Networks in Real-Time.
The 15th Asian Conference on Machine Learning - ACML 2023, 11.11.-14.11.2023.
PMLR, 222:422-437, 2024.
online unter: https://proceedings.mlr.press/v222/herdt24a.html
C. W. Bang, V. Didelez.
Do we become wiser with time? On causal equivalence with tiered background knowledge.
UAI '23: Proceedings of the 39th Annual Conference on Uncertainty in Artifical Intelligence, 2023.
I. Mykhailiuk, C. Büskens.
Parametric Stability Score for Local Solutions of Constrained Parametric Nonlinear Programs.
GAMM 92nd Annual Meeting of the International Association of Applied Mathematics and Mechanics , 15.08-18.08.2022.
Proceedings in Applied Mathematics & Mechanics, 22(1), Wiley, 2023.
DOI: 10.1002/pamm.202200254
M. Nittscher, M. F. Lameter, R. Barbano, J. Leuschner, B. Jin, P. Maa?.
SVD-DIP: Overcoming the Overfitting Problem in DIP-based CT Reconstruction.
Medical Imaging with Deep Learning (MIDL 2023), 10.07.-12.07.2023.
online unter: https://2023.midl.io/papers/p014
A. Osmers, A. Rademacher, A. Schr?der.
Goal-oriented Adaptive Finite Cell Methods.
ENUMATH 2023, 04.09. - 08.09.2023, Lissabon, Portugal.
F. Wiesener, B. Bergmann, M. Wichmann, M. Eden, T. Freudenberg, A. Schmidt.
Modeling of heat transfer in tool grinding for multiscale simulations.
CIRP CMMO 2023, 31.05.-02.06.2023, Karlsruhe, Deutschland.
Procedia CIRP, S. 6 p., 2023.
H. Albers, T. Kluth.
Immobilized nanoparticles with uniaxial anisotropy in multi-dimensional lissajous-type excitation: An equilibrium model approach.
International Workshop on Magnetic Particle Imaging, 21.03.-23.03.2022, University of Würzburg, Deutschland.
International Journal on Magnetic Particle Imaging, 8(1):4 pages, 2022.
DOI: 10.18416/IJMPI.2022.2203048
A. Folkers, C. Wellhausen, M. Rick, X. Li, L. Evers, V. Schwarting, J. Clemens, P. Dittmann, M. Shubbak, T. Bustert, G. Zachmann, K. Schill, C. Büskens.
The OPA3L System and Testconcept for Urban Autonomous Driving.
25th IEEE International Conference on Intelligent Transportation Systems, 08.10.-12.10.2022.
DOI: 10.1109/ITSC55140.2022.9922416
online unter: https://ieeexplore.ieee.org/document/9922416
M. H?ffmann, J. Clemens, D. Stronzek-Pfeifer, R. Simonelli, A. Serov, S. Schettino, M. Runge, K. Schill, C. Büskens.
Coverage Path Planning and Precise Localization for Autonomous Lawn Mowers.
IEEE 6th International Conference on Robotic Computing (IRC), 05.12.-07.12.2022.
Proceedings of International Conference on Robotic Computing, S. 238-242, 2022.
DOI: 10.1109/IRC55401.2022.00046
online unter: https://ieeexplore.ieee.org/document/10023754
M. Nitzsche, H. Albers, T. Kluth, B. Hahn.
Compensating model imperfections during image reconstruction via resesop.
International Workshop on Magnetic Particle Imaging, 21.03.-23.03.2022, University of Würzburg, Deutschland.
International Journal on Magnetic Particle Imaging, 8(1):4 pages, 2022.
DOI: 10.18416/IJMPI.2022.2203062
P. Rink, W. Brannath.
Multiplicity-adjusted confidence intervals for conditional prediction performance measures.
12th International Conference on Multiple Comparison Procedures, Bremen 30.08.- 02.09.2022.
online unter: https://www.mcp-conference.org/wp-content/uploads/sites/2/2022/08/Abstract-Book_MCP2022_final.pdf
A.-M. Blechschmidt, I. Bougoudis, A. Bracher, J.P. Burrows, S. Losa, A. Richter, S. Seo, T. B?sch, M. Zeising, B. Zilker
Feedback of atmospheric composition and ocean colour to Arctic amplification
3d (AC)3 Science Conference, Potsdam, Germany, 25 October 2021 - 27 October 2021 .
online unter: https://epic.awi.de/id/eprint/56559/
E. Dierkes, K. Fla?kamp.
Learning Mechanical Systems by Hamiltonian Neural Networks.
7th IFAC Workshop on Lagrangian and Hamiltonian Methods for Nonlinear Control LHMNC 2021, 11.-13.10.2021.
Proc. Appl.Math. Mech., 20(1).
DOI: 10.1002/pamm.202100116
E. Dierkes, K. Fla?kamp.
Learning Hamiltonian Systems considering System Symmetries in Neural Networks.
7th IFAC Workshop on Lagrangian and Hamiltonian Methods for Nonlinear Control LHMNC 2021, 11.-13.10.2021.
IFAC PapersOnLine 54-19 (2021) 210-216.
online unter: https://www.sciencedirect.com/science/article/pii/S2405896321021042
E. Dierkes, C. Meerpohl, K. Fla?kamp, C. Büskens.
Estimation and Mapping of System-Surface Interaction by Combining Nonlinear Optimization and Machine Learning.
3rd IFAC Conference on Modelling, Identification and Control of Nonlinear Systems MICNON 2021, 15.-17.09.2021.
IFAC PapersOnLine 54-14 (2021) 138-143.
online unter: https://www.sciencedirect.com/science/article/pii/S240589632101747X
M. H?ffmann, S. Roy, A. Berger, W. Bergmann, K. W. Chan, M. Shubbak, J. Langhorst, T. Schnauder, C. Büskens.
Wind Affected Maneuverability of Tugboat-Controlled Ships.
13th IFAC Conference on Control Applications in Marine Systems, Robotics, and Vehicles, 22.09.-24.09.2021.
Proceedings of the 13th IFAC Conference on Control Applications in Marine Systems, Robotics, and Vehicles, 54(16):70-75, 2021.
DOI: 10.1016/j.ifacol.2021.10.075
online unter: https://www.sciencedirect.com/science/article/pii/S2405896321014774
M. Lachmann, C. Büskens.
A Hybrid Approach for Data-Based Models Using a Least-Squares Regression.
OLA 2021 Int. Conf on Optimization and Learning.
DOI: 10.1007/978-3-030-85672-4_5
I. Mykhailiuk, K. Sch?fer, K. Fla?kamp, C. Büskens.
Preferable Minima in Nonlinear Optimization: Definition and Algorithmic Approaches.
13th IFAC Conference on Control Applications in Marine Systems, Robotics, and Vehicles, 22.09.-24.09.2021.
Erscheint in Proceedings of the 13th IFAC Conference on Control Applications in Marine Systems, Robotics, and Vehicles.
online unter: https://hessenbox.uni-kassel.de/dl/fi226HzF3AJV3g4LFWM4fWE6/daily_program_2020.pdf?inline
I. Mykhailiuk, K. Sch?fer, C. Büskens.
Stability Score for Local Solutions of Unconstrained Parametric Nonlinear Programs.
GAMM 92st Annual Meeting of the international Association of Applied Mathematics and Mechanics, online, 15.03.2021 - 19.03.2021.
Proceedings in Applied Mathematics & Mechanics, 21(1), WILEY-VCH, 2021.
DOI: 10.1002/pamm.202100215
M. Schmidt, A. Denker, J. Leuschner.
The Deep Capsule Prior - advantages through complexity.
GAMM 92st Annual Meeting of the international Association of Applied Mathematics and Mechanics, online, 15.03.2021 - 19.03.2021.
Proceedings in Applied Mathematics & Mechanics, 21(1), WILEY-VCH, 2021.
DOI: 10.1002/pamm.202100166
M. Schmidt.
Around the clock - capsule networks and image transformations.
PAMM. Proceedings in Applied Mathematics and Mechanics, 20(1):e202000179, 2021.
DOI: https://doi.org/10.1002/pamm.202000179 online unter: https://onlinelibrary.wiley.com/doi/abs/10.1002/pamm.202000179
S. Seo, A. Richter, A.M. Blechschmidt, I. Bougoudis, F. Wittrock, T. B?sch, J. P. Burrows
Retrieval of tropospheric BrO columns from TROPOMI and their validation using MAX-DOAS measurements in Ny-?lesund
EGU General Assembly 2021, online, 19–30 Apr 2021
DOI: 10.5194/egusphere-egu21-10293
M. Wiesner, K. Sch?fer, W. Bergmann, A. Berger, P. Shulpyakov, C. Dittert, C. Büskens.
Analyzing the Influence of Measurements in Dynamical Parameter Identification Using Parametric Sensitivities.
Third IFAC Conference on Modelling, Identification and Control of Nonlinear Systems, 15.09-17.09.2021.
DOI: 10.1016/j.ifacol.2021.10.320
I. Bougoudis, A.-M. Blechschmidt, A. Richter, S. Seo, J.P. Burrows
Investigating the Relation of Arctic Tropospheric Bro Derived by Satellite Remote Sensing to Sea Ice Age and Meteorological Driving Mechanisms Under the Impact of Arctic Amplification
EGU General Assembly 2020, online04.-08.05.2020.
DOI: 10.5194/egusphere-egu2020-16914
A. Denker, M. Schmidt, J. Leuschner, P. Maa?, J. Behrmann.
Conditional Normalizing Flows for Low-Dose Computed Tomography Image Reconstruction.
ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models, 18.07-18.07.2020, Wien, ?sterreich.
online unter: https://invertibleworkshop.github.io/accepted_papers/index.html
L. Evers.
Benchmarking pre-trained Encoders for real-time Semantic Road Scene Segmentation.
GAMM 91st Annual Meeting of the international Association of Applied Mathematics and Mechanics, Kassel, 15.03.2020 - 19.03.2020.
M. Lachmann, F. Jung, C. Büskens.
Computationally efficient identification of databased models applied to a milk cooling system.
Conference of Computational Interdisciplinary Science, CCIS, 19.03.-22.03.2019, Atlanta, USA.
Campinas: Galoa, 2020.
A. Denker, M. Schmidt, J. Leuschner, P. Maa?, J. Behrmann.
Conditional Normalizing Flows for Low-Dose Computed Tomography Image Reconstruction.
ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models, 18.07-18.07.2020, Wien, ?sterreich.
online at: https://invertibleworkshop.github.io/accepted_papers/index.html
E. Dierkes, F. Jung, C. Büskens.
Data-based models of drive technology for automation in automotive production.
GAMM 91st Annual Meeting of the international Association of Applied Mathematics and Mechanics, Kassel, 15.03.2020 - 19.03.2020.
S. Dittmer, T. Kluth, D. Otero Baguer, B. Maass.
A Deep Prior Approach to Magnetic Particle Imaging.
Machine Learning for Medical Image Reconstruction 2020.
Springer International Publishing, F. Deeba, P. Johnson, T. Würfl, J. C. Ye (Hrsg.), S. 113-122, 2020.
DOI: 10.1007/978-3-030-61598-7_11
M. M?ddel, F. Griese, T. Kluth, T. Knopp.
Estimating orientation using multi-contrast MPI.
10th International Workshop on Magnetic Particle Imaging 2020, Würzburg, 07.09.-09.09.2020.
Infinite Science Publishing, T. Knopp, T. M. Buzug (Eds.), 6(2) pp. 2 pages.
DOI: 10.18416/IJMPI.2020.2009023
H. Mohn, D. Kreyling, I. Wohltmann, M. Barschke, P. Maass, M. Rex.
Estimating the Rate of Change of Stratospheric Ozone using Deep Neural Networks.
Earth and Space Science Open Archive. 2020
DOI: 10.1002/essoar.10505061.2
F. Tramer, J. Behrmann, N. Carlini, N. Papernot, J. Jacobsen.
Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations.
International Conference on Machine Learning (ICML), 12.07 - 18.07.2020, Wien, ?sterreich.
online at: https://arxiv.org/abs/2002.04599
H. Albers, T. Kluth, T. Knopp.
MNPDynamics: A computational toolbox for simulating magnetic moment behavior of ensembles of nanoparticles.
10th International Workshop on Magnetic Particle Imaging 2020, Würzburg, 07.09.-09.09.2020.
Infinite Science Publishing, T. Knopp, T. M. Buzug (Eds.), 6(2) Suppl. 1, 2020.
Article ID 2009020.
DOI: 10.18416/IJMPI.2020.2009020
M. Runge, K. Fla?kamp, C. Büskens.
Model Predictive Control with Online Nonlinear Parameter Identification for a Robotic System.
International Conference on Control, Decision and Information Technologies (CoDIT), 29.06.-02.07.2020, Prag, Tschechien.
Proceedings of CoDIT, 7th International Conference on Control, Decision and Information Technologies (CoDIT), 2020, pp. 312-318.
DOI: 10.1109/CoDIT49905.2020.9263886
I. Mykhailiuk, K. Sch?fer, K. Fla?kamp, C. Büskens.
On the Computation of Convergence Regions for Sequential Nonlinear Programming Problems.
GAMM 91st Annual Meeting of the international Association of Applied Mathematics and Mechanics, Kassel, 15.03.2020 - 19.03.2020.
PAMM 20(1), 2021.
I. Mykhailiuk, K. Sch?fer, K. Fla?kamp, C. Büskens.
Preferable Minima in Nonlinear Optimization: Definition and Algorithmic Approaches.
ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models, 18.07-18.07.2020, Wien, ?sterreich.
online at: https://hessenbox.uni-kassel.de/dl/fi226HzF3AJV3g4LFWM4fWE6/daily_program_2020.pdf?inline
M. Runge, K. Fla?kamp, C. Büskens.
Real-time parameter estimation for sensitivity-based LQ regulator adaptation .
GAMM 91st Annual Meeting of the international Association of Applied Mathematics and Mechanics, Kassel, 15.03.2020 - 19.03.2020.
K. Sch?fer, J. Fliege, K. Fla?kamp, C. Büskens.
Reformulating Bilevel Problems by SQP Embedding.
GAMM 91st Annual Meeting of the international Association of Applied Mathematics and Mechanics, Kassel, 15.03.2020 - 19.03.2020.
T. Kluth, P. Szwargulski, T. Knopp.
Towards accurate modeling of the multidimensional MPI physics.
10th International Workshop on Magnetic Particle Imaging 2020, Würzburg, 07.09.-09.09.2020.
Infinite Science Publishing, T. Knopp, T. M. Buzug (Eds.), 6(2) pp. 2 pages.
DOI: 10.18416/IJMPI.2020.2009004
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 at: https://sites.google.com/view/mlwithguarantees/accepted-papers
I. Bougoudis, A.-M. Blechschmidt, A. Richter, S. Seo, J.P. Burrows
Long-term Time-series of Arctic BrO Derived from Satellite Remote Sensing and its Relation to Driving Mechanisms under the Impact of Arctic Amplification
EGU General Assembly 2019, 07.-12. April 2019, Vienna, Austria
online unter: https://www.iup.uni-bremen.de/doas/posters/egu_2019_bougoudis.pdf
T. Czotscher, D. Otero Baguer, F. Vollertsen, I. Piotrowska-Kurczewski, P. Maa?.
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
K. Sch?fer, K. Fla?kamp, 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, ?sterreich.
90, Proc. Appl. Math. Mech., 19, Wiley, 2019.
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, ?sterreich.
Proc. Appl. Math. Mech., 19(1):e201900374, 2019.
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
T. Czotscher, D. Otero Baguer, F. Vollertsen, I. Piotrowska-Kurczewski, P. Maa?.
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
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, pp. 2025-2031, 2019.
**Best Student Paper
J. Jacobsen, J. Behrmann, R. Zemel, M. Bethge.
Excessive Invariance Causes Adversarial Vulnerability.
International Conference on Learning Representations (ICLR), 2019.
online at: https://openreview.net/forum?id=BkfbpsAcF7
J. Jacobsen, J. Behrmann, N. Carlini, F. Tramer, N. Papernot.
Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness.
SafeML Workshop, ICLR, 2019.
online at: https://arxiv.org/abs/1903.10484
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
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, pp. 6747-6756, 2019.
online at: http://proceedings.mlr.press/v97/westphal19a.html
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 at: http://proceedings.mlr.press/v97/behrmann19a.html
C. Etmann, S. Lunz, P. Maa?, C. Sch?nlieb.
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 at: http://proceedings.mlr.press/v97/etmann19a.html
R. T. Chen, J. Behrmann, D. Duvenaud, J. Jacobsen.
Residual Flows for Invertible Generative Modeling.
Advances in Neural Information Processing Systems (NeurIPS).
32, pp. 9916--9926, 2019.
**Spotlight
online at: https://papers.nips.cc/paper/9183-residual-flows-for-invertible-generative-modeling
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, pp. 23-24, Infinite Science Publishing, 2019.
K. Sch?fer, K. Fla?kamp, C. Büskens.
A Numerical Study of the Robustness of Transcription Methods for Parameter Identification Problems.
GAMM Annual Meeting of the international Association of Applied Mathematics and Mechanics, 19.03.-23.03.2018, München, Deutschland.
89, Proc. Appl. Math. Mech., 18, Wiley, 2018.
L. Sommer, M. Rick, A. Folkers, C. Büskens.
AO-Car: Transfer of Space Technology to Autonomous Driving with the use of WORHP.
7th International Conference on Astrodynamics Tools and Techniques, 2018.
J. Clemens, C. Meerpohl, V. Schwarting, M. Rick, K. Schill, C. Büskens.
Autonomous In-Ice Exploration of the Saturnian Moon Enceladus.
69th International Astronautical Congress (IAC), 01.10.-05.10.2018, Bremen, Deutschland.
T. Kluth, B. Jin.
Exploiting Ill-Posedness in Magnetic Particle Imaging - System Matrix Approximation via Randomized SVD.
International Workshop on Magnetic Particle Imaging 2018.
International Workshop on Magnetic Particle Imaging (IWMPI) Book of Abstracts 2018, pp. 127-128, Infinite Science Publishing, 2018.
J. Fl?totto, T. Kluth, M. M?ddel, T. Knopp, P. Maa?.
Improving Generalization Properties of Measured System Matrices by Using Regularized Total Least Squares Reconstruction in MPI.
International Workshop on Magnetic Particle Imaging 2018.
International Workshop on Magnetic Particle Imaging (IWMPI) Book of Abstracts 2018, pp. 53-54, Infinite Science Publishing, 2018.
W. Heins, C. Büskens.
Two-Level Forecast-Based Energy and Load Management for Grid-Connected Local Systems Using General Load and Storage Models.
18th International Conference on Environment and Electrical Engineering (EEEIC), 12.06-15.06.2018, Palermo, Italien
D. Otero Baguer, I. Piotrowska, P. Maa?.
Inverse Problems in designing new structural materials.
7th International Conference on High Performance Scientific Computing, 19.03-23.03.2018, Hanoi, Vietnam.
P. Gralla, I. Piotrowska, D. . Rippel, M. Lütjen, P. Maa?.
Inverting Prediction Models in Micro Production for Process Design.
5TH INTERNATIONAL CONFERENCE ON NEW FORMING TECHNOLOGY, 18.09.-21.09.2018, Bremen, Deutschland.
DOI: 10.1051/matecconf/201819015007
C. Bathke, T. Kluth, P. Maa?.
MPI Reconstruction Using Structural Prior Information and Sparsity.
International Workshop on Magnetic Particle Imaging 2018.
International Workshop on Magnetic Particle Imaging (IWMPI) Book of Abstracts 2018, pp. 129-130, Infinite Science Publishing, 2018.
C. Meerpohl, K. Fla?kamp, C. Büskens.
Optimization Strategies for Real-Time Control of an Autonomous Melting Probe.
2018 American Control Conference (ACC), 2018, Milwaukee, WI, USA.
DOI: 10.23919/ACC.2018.8430877
K. Sch?fer, M. Runge, K. Fla?kamp, C. Büskens.
Parameter Identification for Dynamical Systems Using Optimal Control Techniques.
European Control Conference (ECC) 2018, 12.06.-15.06.2018, Limassol, Zypern.
DOI: 10.23919/ECC.2018.8550045
M. Runge, K. Fla?kamp, C. Büskens.
Sequential Solution of Parameter Identification and Optimal Control Problems for Robotic Systems.
89th GAMM Annual Meeting of the international Association of Applied Mathematics and Mechanics (GAMM), 19.03.-23.03.2018, München, Deutschland.
Proc. Appl. Math. Mech., 2018.
K. Tracht, A. Onken, P. Gralla, J. H. Emad, N. Kipry, P. Maa?.
Trend-specific clustering for micro mass production of linked parts.
CIRP General Assembly 2018, 19.08-25.08.2018.
CIRP Annals, Manufacturing Technology, 67(1):9-12, Elsevier, 2018.
DOI: 10.1016/j.cirp.2018.04.017
W. Heins, C. Büskens.
Two-Level Forecast-Based Energy and Load Management for Grid-Connected Local Systems Using General Load and Storage Models.
18th International Conference on Environment and Electrical Engineering (EEEIC), 12.06-15.06.2018, Palermo, Italien.
K. Fla?kamp, K. Sch?fer, C. Büskens.
Variational Integrators for Parameter Identification of Mechanical Systems.
GAMM Annual Meeting of the international Association of Applied Mathematics and Mechanics, 19.03.-23.03.2018, München, Deutschland.
89, Proc. Appl. Math. Mech., 18, Wiley, 2018.
C. Bathke, T. Kluth, C. Brandt, P. Maa?.
Improved image reconstruction in magnetic particle imaging using structural a priori information.
International Workshop on Magnetic Particle Imaging 2017.
International Workshop on Magnetic Particle Imaging (IWMPI) Book of Abstracts 2017, pp. 85, Infinite Science Publishing, 2017.
T. Kluth, P. Maa?.
Model uncertainty in magnetic particle iamging: Motivating nonlinear problems by model-based sparse reconstruction.
International Workshop on Magnetic Particle Imaging 2017.
International Workshop on Magnetic Particle Imaging (IWMPI) Book of Abstracts 2017, pp. 83, Infinite Science Publishing, 2017.
F. Jung, M. Lachmann, C. Büskens.
SmartFarm - Data based optimization for optimal energy management.
88th GAMM Annual Meeting of the international Association of Applied Mathematics and Mechanics (GAMM).
Proc. Appl. Math. Mech., 17(1):741-742, 2017.
P. Gralla, I. Piotrowska-Kurczewski, P. Maa?.
Tikhonov Functionals Incorporating Tolerances.
88th GAMM Annual Meeting of the international Association of Applied Mathematics and Mechanics (GAMM).
To appear in Proc. Appl. Math. Mech.
P. Gralla, I. Piotrowska-Kurczewski, P. Maa?.
Parameter identification for micro milling processes using inverse problems incorporating tolerances.
International Congress on engineering, design and Manufacturing 2016, 08.09-10.09.2016, Barcelona, Spanien.
M. Schmidt.
Around the clock - capsule networks and image transformations.
PAMM.
To appear in Proceedings in Applied Mathematics and Mechanics.
P. Gralla, I. Piotrowska-Kurczewski, D. . Rippel, M. Lütjen, P. Maa?.
Eine Methode zur Invertierung von Vorhersagemodellen in der Mikrofertigung.
8. Kolloquium Mikroproduktion, 27.11.-28.11.2017, Bremen, Deutschland.
J. Behrmann, M. Schmidt, J. Wildner, P. Maa?, S. Schmale.
Purity Assessment of Pellets Using Deep Learning.
German Success Stories in Industrial Mathematics, H. Bock, K. Küfer, P. Maa?, A. Milde, V. Schulz (Hrsg.), Mathematics in Industry, S. 29-34, Springer Verlag, 2022.
DOI: 10.1007/978-3-030-81455-7_6
P. Maa?, S. Dittmer, T. Kluth, J. Leuschner, M. Schmidt.
Mathematische Architekturen für Neuronale Netze.
Erfolgsformeln – Anwendungen der Mathematik, M. Ehrhardt, M. Günther, W. Schilders (Hrsg.), Mathematische Semesterberichte, S. 190-195, Springer Verlag, 2022.
DOI: 10.1007/s00591-022-00325-y
T. Gerken.
Dynamic Inverse Problems for the Acoustic Wave Equation.
Time-dependent Problems in Imaging and Parameter Identification, Time-dependent Problems in Imaging and Parameter Identification, Springer Verlag, 2020.
S. G?rres, S. B?ttcher, P. Rink, W. Brannath.
StaVaCare 2.0 - Zusammenh?nge zwischen Care-, Case-Mix, Organisation und Qualit?t in Pflegeheimen.
Schriftenreihe zur Weiterentwicklung der Pflegeversicherung, GKV Spitzenverband, 2020.
L. Siemer, I. Sch?fer, J.D. Rademacher, M. Ke?eb?hmer.
Von explorierenden Aufgaben bis zur Mitarbeit im Forschungsteam – Forschungsgelegenheiten im Bachelorstudiengang Mathematik.
In: Hoffmeister, T., Koch, H., Tremp, P. (eds) Forschendes Lernen als Studiengangsprofil. Springer VS, Wiesbaden
DOI: 10.1007/978-3-658-28825-9_7
O. Riemer, P. Maa?, F. E. Elsner-D?rge, P. Gralla, J. Vehmeyer, M. Willert, A. Meier, I. Zahn.
Predictive compensation measures for the prevention of shape deviations of mircomilled dental products.
Cold Micro Metal Forming, Springer Verlag, 2018.
B. Denkena, P. Maa?, A. Schmidt, D. Niederwestberg, J. Vehmeyer, C. Niebuhr, P. Gralla.
Thermomechanical Deformation of Complex Workpieces in Milling and Drilling Processes.
Thermal Effects in Complex Machining Processes - Final Report of the DFG Priority Program 1480, D. Biermann, F. Hollmann (Eds.), LNPE, pp. 219-250, Springer Verlag, 2017.
Christine Winther Bang
Constraint-based causal discovery with tiered background knowledge (submitted)
Dissertationsschrift, Universit?t Bremen, 2025.
Alexander Denker
Invertible Neural Networks and Normalizing Flows for Image Reconstruction
Dissertationsschrift, Universit?t Bremen, 2024.
online at: https://media.suub.uni-bremen.de/handle/elib/7839
Louisa Kinzel
Unsupervised Deep Machine Learning Methods to Discriminate IceQuakes in Seismological Data from Neumayer Station, Antarctica
Dissertationsschrift, Universit?t Bremen, 2024.
online at: https://media.suub.uni-bremen.de/handle/elib/7970
Gideon Klaila
The persistence transformation; a new methodology of topological data analysis
Dissertationsschrift, Universit?t Bremen, 2024.
online at: https://doi.org/10.26092/elib/2747
Vladimir Vutov
Large-scale multiple testing under arbitrary covariance dependency and topological data analysis for mass spectrometry imaging applications
Dissertationsschrift, Universit?t Bremen, 2024.
online at: https://doi.org/10.26092/elib/2770
Margareta Runge
Online parameter identification for optimal feedback control of nonlinear dynamical systems
Dissertationsschrift, Universit?t Bremen, 2024.
online at: https://doi.org/10.26092/elib/2743
Kai Sch?fer
Decomposition methods for parameter identification and bilevel programming
Dissertationsschrift, Universit?t Bremen, 2023.
online at: https://doi.org/10.26092/elib/2735
Johannes Leuschner
Deep Learning for Computed Tomography Reconstruction - Learned Methods, Deep Image and Uncertainty Estimation
Dissertationsschrift, Universit?t Bremen, 2023.
online at: https://doi.org/10.26092/elib/2704
Phil Gralla
Tikhonov Functionals Incorporating Tolerances in Discrepancy Term for Inverse Problems
Dissertationsschrift, Universit?t Bremen, 2023.
online at: https://doi.org/10.26092/elib/2097
Maximilian Schmidt
Hybrid Deep Learning - How Combining Data-Driven and Model-Based Approaches Solves Inverse Problems in Computed Tomography and Beyond
Dissertationsschrift, Universit?t Bremen, 2022.
online at: https://doi.org/10.26092/elib/1941
Anh-Tuan Hoang
Statistische Methoden zur Replizierbarkeitsbewertung im Rahmen 澳门皇冠_皇冠足球比分-劲爆体育stufiger Studien
Dissertationsschrift, Universit?t Bremen, 2022.
online at: https://doi.org/10.26092/elib/1531
Pascal Fernsel
Nonnegative matrix factorization - theory, algorithms and applications
Dissertationsschrift, Universit?t Bremen, 2022.
online at: https://doi.org/10.26092/elib/1493
S?ren Schulze
Blind source separation in single-channel polyphonic music recordings
Dissertationsschrift, Universit?t Bremen, 2022.
online at: https://doi.org/10.26092/elib/1439
Jonathan von Schroeder
Non-parametric Statistical Methods - Applications in MALDI Imaging and Finance
Dissertationsschrift, Universit?t Bremen, 2022.
online at: https://doi.org/10.26092/elib/1415
Miriam Steinherr Zazo
Bifurcation Analysis for Systems with Piecewise Smooth Nonlinearity and Applications
Dissertationsschrift, Universit?t Bremen, 2021.
online at: https://doi.org/10.26092/elib/1407
Ilias Bougoudis
Satellite based remote sensing of halogens in the Arctic troposphere, under the impact of Arctic Amplification
Dissertationsschrift, Universit?t Bremen, 2021.
online at: https://doi.org/10.26092/elib/1115
Georgia Sfakianaki
Regularization of ill-posed inverse problems with tolerances and sparsity in the parameter space
Dissertationsschrift, Universit?t Bremen, 2021.
online at: https://doi.org/10.26092/elib/1065
T. Kluth
Model-based to data-driven approaches for parameter identification and image reconstruction in the applied inverse problem of magnetic particle imaging (submitted)
Habilitationsschrift, Universit?t Bremen, 2020.
S. Dittmer
On deep learning applied to inverse problems - A chicken-and-egg problem (submitted)
Dissertationsschrift, Universit?t Bremen, 2020.
C. Etmann.
Double Backpropagation with Applications to Robustness and Saliency Map Interpretability.
Dissertationsschrift, Universit?t Bremen, 2020.
D. Otero Baguer.
Neural Networks for solving Inverse Problems. Applications in Materials Science and Medical Imaging.
Dissertationsschrift, Universit?t Bremen, 2020.
A. Konschin.
Direkte und inverse elektromagnetische Streuprobleme für lokal gest?rte periodische Medien.
Dissertationsschrift, Universit?t Bremen, 2019.
online at: http://nbn-resolving.de/urn:nbn:de:gbv:46-00107835-13
T. Gerken.
Dynamic Inverse Problems for Wave Phenomena.
Dissertationsschrift, Universit?t Bremen, 2019.
online at: https://nbn-resolving.de/urn:nbn:de:gbv:46-00107730-18
M. Westphal.
Model Selection and Evaluation in Supervised Machine Learning.
Dissertationsschrift, Universit?t Bremen, 2019.
DOI: https://doi.org/10.26092/elib/16
J. Behrmann.
Principles of Neural Network Architecture Design: Invertibility and Domain Knowledge.
Dissertationsschrift, Universit?t Bremen, 2019.
online at: https://elib.suub.uni-bremen.de/peid/D00108536.html
S. Saha.
Multiple testing and modeling in dose-response studies.
Dissertationsschrift, Universit?t Bremen, 2018.
H. Albers, T. Kluth.
Time-dependent parameter identification in a Fokker-Planck equation based magnetization model of large ensembles of nanoparticles.
Zur Ver?ffentlichung eingereicht.
online unter: https://arxiv.org/abs/2307.03560
C. Arndt, J. Nickel.
Invertible ResNets for inverse imaging problems: Competitive performance with provable regularization properties.
Zur Ver?ffentlichung eingereicht.
online unter: https://arxiv.org/abs/2409.13482.
R. Barbano, A. Denker, H. Chung, T. Roh, S. Arrdige, P. Maass, B. Jin, J. Ye.
Steerable conditional diffusion for out-of-distribution adaptation in imaging inverse problems.
arXiv: arxiv.org/abs/2308.14409, Under Review.
R. Barbano, J. Antorán, J. Leuschner, J.M. Hernández-Lobato, ?. Kereta, B. Jin (2023)
Fast and Painless Image Reconstruction via Deep Image Prior Subspaces.
arXiv preprint, under review (arXiv:2302.10279)
R. Barbano, J. Leuschner, J. Antorán, B. Jin, J. M. Hernández-Lobato.
Bayesian Experimental Design for Computed Tomography with the Linearised Deep Image Prior.
Zur Ver?ffentlichung eingereicht.
online unter: https://arxiv.org/abs/2207.05714
Beckmann, M. and Heilenk?tter, N. (2023)
Equivariant neural networks for indirect measurements,
Preprint available at arXiv:2306.16506
J. Behrmann, P. Vicol, K. Wang, R. Grosse, J. Jacobsen.
Understanding and Mitigating Exploding Inverses in Invertible Neural Networks.
Zur Ver?ffentlichung eingereicht.
online at: https://arxiv.org/abs/2006.09347
J. Behrmann, S. Dittmer, P. Fernsel, P. Maa?.
Analysis of Invariance and Robustness via Invertibility of ReLU-Networks.
Zur Ver?ffentlichung eingereicht
online at: https://arxiv.org/abs/1806.09730
T. Dickhaus, R. Heller, A.T. Hoang.
Multiple testing of partial conjunction null hypotheses with conditional p-values based on combination test statistics..
Zur Ver?ffentlichung eingereicht.
online at: https://arxiv.org/abs/2110.06692
S. Dittmer, D. Erzmann, H. Harms, P. Maa?.
SELTO: Sample-Efficient Learned Topology Optimization.
Zur Ver?ffentlichung eingereicht.
online unter: https://arxiv.org/abs/2209.05098
S. Dittmer, T. Kluth, M. Henriksen, P. Maa?.
Deep image prior for 3D magnetic particle imaging: A quantitative comparison of regularization techniques on Open MPI dataset.
Zur Ver?ffentlichung eingereicht.
online at: https://arxiv.org/abs/2007.01593
S. Dittmer, P. Maa?.
A Projectional Ansatz to Reconstruction.
Zur Ver?ffentlichung eingereicht.
online at: https://arxiv.org/abs/1907.04675
S. Dittmer, M. Roberts, J. Preller, .. AIX-COVNET Collaboration, J. H. F. Rudd, J. A. D. Aston, C. Sch?nlieb.
Reinterpreting survival analysis in the universal approximator age.
Zur Ver?ffentlichung eingereicht.
M. Eden, T. Freudenberg, A. Muntean.
Precomputing approach for a two-scale phase transition model.
Zur Ver?ffentlichung eingereicht.
online unter: https://arxiv.org/abs/2407.21595
C. Etmann.
A Closer Look at Double Backpropagation.
Zur Ver?ffentlichung eingereicht.
online at: https://arxiv.org/abs/1906.06637
C. Etmann, M. Schmidt, J. Behrmann, T. Boskamp, L. Hauberg-Lotte, A. Peter, R. Casadonte, J. Kriegsmann, P. Maa?.
Deep Relevance Regularization: Interpretable and Robust Tumor Typing of Imaging Mass Spectrometry Data.
Zur Ver?ffentlichung eingereicht.
online at: https://arxiv.org/abs/1912.05459
P. Fernsel,P. Maass.
Regularized Orthogonal Nonnegative Matrix Factorization and K-means Clustering.
Zur Ver?ffentlichung eingereicht.
online unter: https://arxiv.org/abs/2112.07641
T. Freudenberg, M. Eden.
Analysis and Simulation of a Coupled Fluid-Heat System in a Thin, Rough Layer.
Zur Ver?ffentlichung eingereicht.
online unter: https://arxiv.org/abs/2406.02150
J. G?deke, P. Fernsel.
New universal operator approximation theorem for encoder-decoder architectures (Preprint).
Zur Ver?ffentlichung eingereicht.
online unter: https://arxiv.org/abs/2503.24092
T. Grossmann, S. Dittmer, Y. Korolev, C. Sch?nlieb.
Unsupervised Learning of the Total Variation Flow.
Zur Ver?ffentlichung eingereicht.
online unter: https://arxiv.org/abs/2206.04406#
R. Grotheer, T. . Strauss, P. Gralla, T. Khan.
Alternatives for Generating a Reduced Basis to Solve the Hyperspectral Diffuse Optical Tomography Model.
Zur Ver?ffentlichung eingereicht.
online at: https://arxiv.org/abs/1803.00948
R. Herdt, M. Schmidt, D. Otero Baguer, J. Le Clerc Arrastia, P. Maa?.
Model Stitching and Visualization How GAN Generators can Invert Networks in Real-Time.
Zur Ver?ffentlichung eingereicht.
online unter: https://arxiv.org/abs/2302.02181
M. Iske, H. Albers, T. Knopp, T. Kluth.
Learned Discrepancy Reconstruction and Benchmark Dataset for Magnetic Particle Imaging.
Zur Ver?ffentlichung eingereicht.
online unter: https://arxiv.org/html/2501.05583v1
G. Klaila, L. Ranke, A. Stefanou (2023).
Stability of the Persistence Transformation,
https://arxiv.org/pdf/2310.05559.pdf
T. Kluth, H. Albers.
Simulation of non-linear magnetization effects and parameter identification problems in magnetic particle imaging.
Erscheint in Oberwolfach Reports
T. Kluth, C. Bathke, M. Jiang, P. Maa?.
Joint super-resolution image reconstruction and parameter identification in imaging operator: Analysis of bilinear operator equations, numerical solution, and application to magnetic particle imaging.
Zur Ver?ffentlichung eingereicht.
online at: https://arxiv.org/abs/2004.13091
T. Kluth, B. Jin.
L1 data fitting for robust reconstruction in magnetic particle imaging: quantitative evaluation on Open MPI dataset.
Zur Ver?ffentlichung eingereicht.
online at: https://arxiv.org/abs/2001.06083
T. Kluth, B. Jin.
Exploiting heuristic parameter choice rules for one-click image reconstruction in magnetic particle imaging.
Zur Ver?ffentlichung eingereicht.
J. Leuschner, M. Schmidt, D. Otero Baguer, P. Maa?.
The LoDoPaB-CT Dataset: A Benchmark Dataset for Low-Dose CT Reconstruction Methods.
Zur Ver?ffentlichung eingereicht.
online at: arXiv:1910.01113
T. Lütjen, F. Sch?nfeld, J. Leuschner, M. Schmidt, A. Wald, T. Kluth.
Learning-based approaches for reconstructions with inexact operators in nanoCTapplications.
Zur Ver?ffentlichung eingereicht.
online unter: https://aps.arxiv.org/abs/2307.10474
S. Mukherjee, S. Dittmer, Z. . Shumaylov, S. Lunz, O. ?ktem, C. Sch?nlieb.
Learned convex regularizers for inverse problems.
Zur Ver?ffentlichung eingereicht.
online unter: https://arxiv.org/abs/2008.02839
M. Nittscher, M.F. Lameter, R. Barbano, J. Leuschner, B. Jin, P. Maa? (2023)
SVD-DIP: Overcoming the Overfitting Problem in DIP-based CT Reconstruction.
Accepted at Medical Imaging with Deep Learning conference (arXiv:2303.15748)
I. Piotrowska-Kurczewski, G. Sfakianaki.
Tikhonov functionals with a tolerance measure introduced in the regularization.
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online at: http://arxiv.org/abs/2007.06431
M. Roberts, A. Hazan, S. Dittmer, J. H. F. Rudd, C. Sch?nlieb.
The curious case of the test set AUROC.
Zur Ver?ffentlichung eingereicht.
T. Schierenbeck, V. Vutov, T. Dickhaus, M. Beetz.
Integrating Transformations in Probabilistic Circuits.
Zur Ver?ffentlichung eingereicht.
online unter: https://arxiv.org/pdf/2310.04354
S. Schulze, E. King.
Formulating Beurling LASSO for Source Separation via Proximal Gradient Iteration.
Zur Ver?ffentlichung eingereicht.
online unter: https://doi.org/10.48550/arXiv.2202.08082
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.
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DOI: 10.5194/acp-2019-996
M. Steinherr Zazo, J.D.M. Rademacher.
Nonlinear effects of stabilization in ship models with non-smooth nonlinearities using P-control.
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online unter: https://arxiv.org/abs/2104.10663
J. von Schroeder.
Stable Feature Selection with Applications to MALDI Imaging Mass Spectrometry Data.
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online at: https://arxiv.org/abs/2006.15077
M. Westphal.
Simultaneous Inference for Multiple Proportions: A Multivariate Beta-Binomial Model.
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Dennis Zvegincev (March 2022).
A Tikhonov approach to level set curvature computation.
ArXiv, abs/2203.12558. [https://doi.org/10.48550/arXiv.2203.12558]
C. Brandt, M. Hamann, J. Leuschner.
Regression Models for Ultrasonic Testing of Carbon Fiber Reinforced Polymers.
Berichte aus der Technomathematik 19–01, Universit?t Bremen, 2019.
J. von Schroeder, T. Dickhaus, T. Bodnar.
Reverse Stress Testing in Skew-Elliptical Models.
Research Report 2019:04, 2019.
online at: Mathematical Statistics, Stockholm University
A. Folkers.
Steuerung eines autonomen Fahrzeugs durch Deep Reinforcement Learning.
BestMasters, 75 pages, Springer Verlag, Universit?t Bremen, 2019.
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, Universit?t Bremen, Juni 2019.
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, Universit?t Bremen, Januar 2019.
F. Jung, M. Lachmann, W. Heins, D. Weigel, J. Maldonado, G. Meyer, N. Koop, N. Steenhusen, J. N. Hasse, M. Echim, C. Büskens, K. Schill, R. Frase.
SmartFarm: Datenbasiert zum optimierten Eigenverbrauch.
Zwischenbericht, 02/ 2017, Bremen, Universit?t Bremen, Februar 2018.
F. Jung, M. Lachmann, W. Heins, J. Maldonado, G. Meyer, N. Koop, N. Steenhusen, J. N. Hasse, M. Echim, C. Büskens, K. Schill, R. Frase.
SmartFarm: Datenbasiert zum optimierten Eigenverbrauch.
Zwischenbericht, 01/ 2018, Bremen, Universit?t Bremen, August 2018.
F. Jung, M. Lachmann, W. Heins, D. Weigel, J. Maldonado, G. Meyer, C. Fiege, N. Koop, N. Steenhusen, J. N. Hasse, M. Echim, C. Büskens, K. Schill, R. Frase.
SmartFarm: Datenbasiert zum optimierten Eigenverbrauch.
Zwischenbericht, 01/ 2017, Bremen, Universit?t Bremen, Juli 2017.
F. Jung, M. Lachmann, W. Heins, D. Weigel, C. Zschippig, G. Meyer, J. Frels, C. Fiege, N. Koop, M. Runge, J. N. Hasse, M. Echim, C. Büskens, K. Schill, R. Frase.
Verbundvorhaben: SmartFarm: Datenbasiert zum optimierten Eigenverbrauch.
Zwischenbericht, 02/ 2016, Bremen, Universit?t Bremen, Januar 2017.
F. Jung, W. Heins, D. Weigel, C. Zschippig, G. Meyer, J. Frels, C. Fiege, N. Koop, M. Runge, M. Echim, C. Büskens, K. Schill, R. Frase.
Verbundvorhaben: SmartFarm: Datenbasiert zum optimierten Eigenverbrauch.
Zwischenbericht, 1. Halbjahr 2016, Bremen, Juli 2016.