Dr. Christoph Brauer (Lehrbeauftragter)

Dr. Christoph Brauer

Contact Details

christoph brauer image

Email:                              ch.brauer(at)tu-bs.de

Office Hours:                 by arrangement

 

I am a staff scientist at DLR, a lecturer at TU Braunschweig, and an external member of the Inverse Problems and Imaging group at the Center for Industrial Mathematics, University of Bremen. My research interests include mathematical optimization, machine learning, and image and signal processing, with a focus on real-world applications in lightweight engineering.

Publications

  • Investigation of Fiber Volume Fraction as Key Parameter in Cryogenic Hydrogen Tank Development
    With Jonas Appels, Philipp Sämann, Jonas Naumann, Daniel Stefaniak, Bilim Atli-Veltin und Clemens Dransfeld. SAMPE 2025 (accepted for publication).
  • Enhancing Composite Micrograph Analysis with Semantic Segmentation
    With Jonas Naumann, Jonas Appels, Philipp Sämann and Timo de Wolff. SAMPE 2025 (accepted for publication).
  • Learning variational models with unrolling and bilevel optimization
    With Niklas Breustedt, Dirk Lorenz and Timo de Wolff. Analysis and Applications, 2024. DOI: 10.1142/S0219530524400037
  • Asymptotic Analysis and Truncated Backpropagation for the Unrolled Primal-Dual Algorithm
    With Dirk Lorenz. 2023 31st European Signal Processing Conference (EUSIPCO). DOI: 10.23919/EUSIPCO58844.2023.10289985
  • Group equivariant networks for leakage detection in vacuum bagging
    With Dirk Lorenz and Lionel Tondji. 2022 30th European Signal Processing Conference (EUSIPCO). DOI: 10.23919/EUSIPCO55093.2022.9909715
  • Learning to dequantize speech signals by primal-dual networks: An approach for acoustic sensor networks
    With Ziyue Zhao, Dirk Lorenz and Tim Fingscheidt. 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). DOI: 10.1109/ICASSP.2019.8683341
  • Complexity and Applications of the Homotopy Principle for Uniformly Constrained Sparse Minimization
    With Dirk A. Lorenz. Applied Mathematics and Optimization, 2019. DOI: 10.1007/s00245-019-09565-2 (Full-text)
  • A Primal-Dual Homotopy Algorithm for ℓ1-Minimization with ℓ∞-Constraints
    With Dirk A. Lorenz and Andreas M. Tillmann. Computational Optimization and Applications, 2018. DOI: 10.1007/s10589-018-9983-4
  • Rank-Optimal Weighting or "How to be Best in the OECD Better Life Index?"
    With Dirk A. Lorenz and Jan Lorenz. Social Indicators Research, 2016. DOI: 10.1007/s11205-016-1416-0
  • Sparse reconstruction of quantized speech signals
    With Dirk A. Lorenz and Timo Gerkmann. 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). DOI: 10.1109/ICASSP.2016.7472817
  • Cartoon-Texture-Noise Decomposition with Transport Norms
    With Dirk A. Lorenz. Scale Space and Variational Methods in Computer Vision: 5th International Conference, SSVM 2015. DOI: 10.1007/978-3-319-18461-6_12

PhD thesis

  • Homotopy Methods for Linear Optimization Problems with Sparsity Penalty and Applications
    Universitätsbibliothek Braunschweig, April 2018. DOI: 10.24355/dbbs.084-201804250929-0

Preprints

  • A Sinkhorn-Newton method for entropic optimal transport
    With Christian Clason, Dirk Lorenz and Benedikt Wirth. arXiv:1710.06635
  • Primal-dual residual networks
    With Dirk Lorenz. arXiv:1806.05823

GitHub

  • ℓ1-Houdini
    Matlab code related to the papers A Primal-Dual Homotopy Algorithm for ℓ1-Minimization with ℓ∞-Constraints and Complexity and Applications of the Homotopy Principle for Uniformly Constrained Sparse Minimization
  • Primal-dual networks
    Python code related to the paper Learning to dequantize speech signals by primal-dual networks: An approach for acoustic sensor networks
  • Leakage Detection
    Python code related to the paper Group equivariant networks for leakage detection in vacuum bagging

Conferences

  • AI4Aerospace 2025, May 19-21, 2025, Toulouse, France
    Talk: Enhancing Composite Micrograph Analysis with Semantic Segmentation (pdf, 4MByte)
  • SIAM IS 2024, May 28-31, 2024, Atlanta, USA
    Talk: Asymptotic analysis and truncated backpropagation for the unrolled primal-dual algorithm (pdf, 2MByte)
  • DLR Wissenschaftstag KI im Systemleichtbau, October 12, 2023, Braunschweig, Germany
    Talk: Intelligente Qualitätssicherung in der Faserverbundfertigung (link)
  • EUSIPCO 2023, September 4-8, 2023, Helsinki, Finland
    Talk: Asymptotic Analysis and Truncated Backpropagation for the Unrolled Primal-Dual Algorithm (pdf, 2MByte)
  • AI4Aerospace 2023, May 30, 2023, Paris, France
    Talk: Data-based leakage detection in the manufacturing of large-scale CFRP components
  • SIAM MDS 2022, September 26-30, 2022, San Diego, USA
    Talk: Asymptotics of unrolled convex optimization algorithms (pdf, 2MByte)
  • EUSIPCO 2022, August 29 - September 2, 2022, Belgrade, Serbia
    Talk: Group equivariant networks for leakage detection in vacuum bagging (pdf, 1MByte)
  • bitkom Roundtable Digitale Luftfahrt, June 29, 2022, Online
    Talk: Ressourceneffizientes Fliegen durch intelligente Qualitätssicherung im Fertigungsprozess
  • ISCM 2021, November 3-4, 2021, Online
    Talk: Localization of leakages in vacuum bagging with volumetric flow meters and recurrent neural networks
  • SIAM MDS20, May 4 - June30, 2020, Online
    Minisymposium: Learning Parameterized Energy Minimization Models - Part I and II
    Co-organized with Michael Möller
    Recorded Talks and Slides: Part I and II
    Talk: Asymptotic Analysis of Unrolled Convex Optimization Algorithms (pdf, 4 MByte)
  • GAMM CoMinDS 2019, October 24-25, 2019, Berlin, Germany
    Poster: Asymptotic analysis of unrolling approaches for bi-level optimization (pdf, 178 KByte)
  • SPARS 2019, July 1-4, 2019, Toulouse, France
    Poster: Ergodic bilevel optimization (pdf, 224 KByte)
    Extended abstract: (pdf, 262 KByte)
    Poster: Exact recovery of partially sparse vectors (pdf, 996 KByte)
    Extended abstract: (pdf, 999 KByte)
  • ICASSP 2019, May 12-17, 2019, Brighton, UK
    Poster: Learning to dequantize speech signals by primal-dual networks: An approach for acoustic sensor networks (pdf, 383 KByte)
  • ISMP 2018, July 1-6, 2018, Bordeaux, France
    Talk: A primal-dual homotopy algorithm for sparse recovery with infinity norm constraints (pdf, 4 MByte)
  • Workshop on Optimization, Machine Learning, and Data Science, April 12-13, 2018, Braunschweig, Germany
    Talk: A primal-dual homotopy algorithm for sparse recovery with infinity norm constraints (pdf, 4 MByte)
  • NIPS 2017, December 4-9, 2017, Long Beach, USA
    Poster: A Sinkhorn-Newton method for entropic optimal transport (pdf, 211 KByte)
    Preprint: arXiv:1710.06635
  • SPARS 2017, June 5-8, 2017, Lisbon, Portugal
    Poster: ℓ1-Houdini: A New Homotopy Method for ℓ1-Minimization (pdf, 308 KByte)
    Extended abstract: (pdf, 220 KByte)
  • ICASSP 2016, March 20-25, 2016, Shanghai, China
    Poster: Sparse Reconstruction of Quantized Speech Signals (pdf, 218 KByte)
  • GAMM 2016, March 7-11, 2016, Braunschweig, Germany
    Talk: Sparse Reconstruction of Quantized Speech Signals (zip, 2 MByte)
  • SPARS 2015, July 6-9, 2015, Cambridge, UK
    Poster: Heuristic Optimality Checks for Noise-Aware Sparse Recovery by ℓ1-Minimization (pdf, 464 KByte)
    Extended abstract: (pdf, 487 KByte)
  • SSVM 2015, May 31-June 4, 2015, Lège Cap Ferret, France
    Talk: Cartoon-Texture-Noise Decomposition with Transport Norms (pdf, 3 MByte)
  • GAMM 2015, March 23-27, 2015, Lecce, Italy
    Talk: Cartoon-Texture-Noise Decomposition with Transport Norms (pdf, 3 MByte)
  • OCIP 2015, March 9-11, 2015, Munich, Germany
    Talk: Cartoon-Texture-Noise Decomposition with Transport Norms (pdf, 2 MByte)

Teaching

  • Winter 2024/25
    Fortgeschrittenenpraktikum Data Science
  • Winter 2023/24
    Maschinelles Lernen und Anwendungen in der Luft- und Raumfahrt
  • Winter 2022/23
    Fortgeschrittenenpraktikum Data Science
  • Winter 2021/22
    Maschinelles Lernen mit neuronalen Netzen
    Fortgeschrittenenpraktikum Data Science
  • Winter 2020/21
    Maschinelles Lernen mit neuronalen Netzen
    Fortgeschrittenenpraktikum Data Science
  • Winter 2019/20
    Maschinelles Lernen mit neuronalen Netzen
    Fortgeschrittenenpraktikum Data Science
  • Summer 2019
    Bachelor-Seminar Angewandte Mathematik
    Master-Seminar Angewandte Mathematik
  • Winter 2018/19
    Maschinelles Lernen mit neuronalen Netzen