AI research
● for the solutions of the future

We apply transformative AI research to real-world challenges, collaborating closely with partners from academia, business, and industry.
Our research partners:
VDE e.V.
KIT
Frauenhofer IKS
DLR
DFKI
Continental
RWTH Aachen
NXP
Fraunhofer IAIS
TUEV South
TRUMPF
IHP
Grammer AG
Giesecke+Devrient Mobile Security
Bosch
Barkhausen Institute
VDE e.V.
KIT
Frauenhofer IKS
DLR
DFKI
Continental
RWTH Aachen
NXP
Fraunhofer IAIS
TUEV South
TRUMPF
IHP
Grammer AG
Giesecke+Devrient Mobile Security
Bosch
Barkhausen Institute
Our mission

AI research for the challenges of today and tomorrow

For us, applied research and technological excellence means that our partners and customers receive solutions to their problems that are both innovative and practicable. In our research projects, we deal with the latest methods of machine learning. We strive to develop general solutions that can be used in a wide range of application areas.

Our main focus is on core German industries, such as manufacturing technologies and mechanical engineering, whose potential for the use of AI is far from exhausted. We are researching how knowledge and data in these areas can soon be used in the same intuitive way as text and image data can be used today.
A man was working on a computer
In contrast to purely academic institutions and industrial R&D, we strive for a balanced connection between these two worlds by taking the freedom to dive deep into relevant research problems while being part of a highly professional and collaborative technical environment.

To add value to the scientific community and advance AI research as a whole, we not only use our results to deliver excellent solutions in the client business, but also publish and present them in leading journals and conferences.
2 people talking at a small table with a laptop and a glass of water between them
At Merantix Momentum, we initiate and participate in a variety of joint partnerships and scientific collaborations across different sectors and funding programs.

We have extensive experience in the design and management of research projects with a particular focus on collaborative research projects. If you are interested in working with us, please get in touch using our contact form!
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Due to the diversity of our customer projects, we try to cover a broad spectrum of machine learning research and always remain at the cutting edge of technology. At the same time, there are some areas on which our research is particularly focused:

- Learning with tabular and time series data, especially load and demand forecasts
- Interpretability, explainability, root cause analysis
- Embedding prior knowledge and domain expertise in learning processes
- Process optimization and simulation-based inference
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Publications

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2024
Explainability and Interpretability in Electric Load Forecasting Using Machine Learning Techniques (2024)
Lukas Baur, Konstantin Ditschuneit, Maximilian Schambach, Can Kaymakci, Thomas Wollmann, Alexander Sauer

Energy and AI

2024
Progressive Updates of Convolutional Neural Networks for Enhanced Reliability in Small Satellite Applications (2024)
Olga Kondrateva, Stefan Dietzel, Maximilian Schambach, Johannes Otterbach, Björn Scheuermann

Elsevier COMCOM Journal

2024
DeepRod: A human-in-the-loop system for automatic rodent behavior analysis (2024)
Adrian Loy, Miha Garafolj, Heike Schauerte, Hanna Behnke, Cyrille Charnier, Philipp Schwarz, Georg Rast, Thomas Wollmann

ICML 2024

2024
Towards Optimal Sobolev Norm Rates for the Vector-Valued Regularized Least-Squares Algorithm (2024)
Zhu Li, Dimitri Meunier, Mattes Mollenhauer, Arthur Gretton

Journal of Machine Learning Research

2023
Interpretable Reinforcement Learning via Neural Additive Models for Inventory Management (2023).
Julien Siems, Maximilian Schambach, Sebastian Schulze, Johannes S. Otterbach

ICLR 2023 - Full PDF at arxiv.org

2023
Uncovering the Inner Workings of STEGO for Safe Unsupervised Semantic Segmentation (2023).
Alexander Koenig, Maximilian Schambach, Johannes S. Otterbach

CVPR 2023 - Full PDF at arxiv.org

2023
SECREDAS: Safe and (Cyber-)Secure Cooperative and Automated Mobility (2023)
Sebastian Gerres

IFAC 2023 - Full PDF at arxiv.org

2023
Curve your Enthusiasm: Concurvity Regularization in Differentiable GAMs (2023)
Julien Siems, Konstantin Ditschuneit, Winfried Ripken, Alma Lindborg, Maximilian Schambach, Johannes Otterbach, Martin Genzel

NeurIPS 2023

2023
Self-distilled Representation Learning for Time Series (2023)
Felix Pieper, Konstantin Ditschuneit, Martin Genzel, Alexandra Lindt, Johannes Otterbach

NeurIPS 2023

2023
Towards Tabular Foundation Models - Status Quo, Challenges, and Opportunities (2023)
Maximilian Schambach

HAL preprint & SAP-internal publication

2023
Multiscale Neural Operators for Solving Time-Independent PDEs (2023)
Winfried Ripken, Lisa Coiffard, Felix Pieper, Sebastian Dziadzio

NeurIPS 2023

2022
Learning the Solution Operator of Boundary Value Problems using Graph Neural Networks (2022).
Winfried Lötzsch, Simon Ohler, Johannes S. Otterbach

ICML 2022 - Full PDF at arxiv.org

2022
Scalable Flow Optimization for Small Satellite Networks using Benders Decomposition (2022).
Olga Kondrateva, Stefan Dietzel, Björn Scheuermann

IEEE WoWMoM 2022 - Full PDF at ieee.org

2022
Towards Learning Self-Organized Criticality of Rydberg Atoms using Graph Neural Networks (2022).
Simon Ohler, Daniel Steven Brady, Winfried Lötzsch, Michael Fleischhauer, Johannes Otterbach

AI4Science Workshop at ICML 2022 - Full PDF at arxiv.org

2022
Auto-Compressing Subset Pruning for Semantic Image Segmentation (2022)
Konstantin Ditschuneit, Johannes S. Otterbach

GCPR 2022 - Full PDF at arxiv.org

2021
MEAL: Manifold Embedding-based Active Learning (2021).
Deepthi Sreenivasaiah, Johannes Otterbach, Thomas Wollmann

CPVR 2021 - Full PDF at arxiv.org

2021
DAAIN: Detection of Anomalous and Adversarial Input using Normalizing Flows (2021).
Samuel von Baußnern, Johannes Otterbach, Adrian Loy, Mathieu Salzmann, Thomas Wollmann

Full PDF at arxiv.org

2021
Chameleon: A Semi-AutoML framework targeting quick and scalable development and deployment of production-ready ML systems for SMEs (2021)
Johannes Otterbach, Thomas Wollmann

Full PDF at arxiv.org

Research projects

Cutting-Edge Results: Successful partnerships in the field of machine learning.

Our research pushes the boundaries. We bring our excellence in machine learning to publicly funded projects covering all industrial sectors. In partnerships with various stakeholders, we explore innovative solutions and discover new approaches to tackle real-world challenges.

Find out what we're working on.

Contact us

  • Focus on research with tabular and time series data
  • Solutions for the industrial challenges of today and tomorrow
  • Integration of machine learning into established and new systems
  • Long-standing, reliable partner in public and private consortium research
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