984 citations
- University of StuttgartDE16 papers
- Fraunhofer Institute for Industrial EngineeringDE2 papers
- Fraunhofer Institute of Optronics, System Technologies and Image ExploitationDE2 papers
- Heilbronn UniversityDE2 papers
- Stuttgart Media UniversityDE2 papers
- Carnegie Mellon UniversityUS1 paper
- Delft University of TechnologyNL1 paper
- Deutscher WetterdienstDE1 paper
- Esslingen University of Applied SciencesDE1 paper
- Forschungszentrum JülichDE1 paper
- Friedrich-Alexander-Universität Erlangen-NürnbergDE1 paper
- Ingenieurgesellschaft Auto und Verkehr (Germany)DE1 paper
34 papers
Physics-Informed Machine Learning in Prognostics and Health Management: A Systematic Literature Review
Christopher Braun, Julian Raible, Marco F. Huber
In modern industry, keeping complex systems reliable, safe, and efficient hinges on Prognostics and Health Management (PHM). Machine Learning (ML) has largely driven advancements i…
Synset Signset Germany: a Synthetic Dataset for German Traffic Sign Recognition
Anne Sielemann, Lena Loercher, Max-Lion Schumacher +3
In this paper, we present a synthesis pipeline and dataset for training / testing data in the task of traffic sign recognition that combines the advantages of data-driven and analy…
From Confusion to Clarity: ProtoScore -- A Framework for Evaluating Prototype-Based XAI
Helena Monke, Benjamin Sae-Chew, Benjamin Fresz +1
The complexity and opacity of neural networks (NNs) pose significant challenges, particularly in high-stakes fields such as healthcare, finance, and law, where understanding decisi…
Data-Efficient Quantum Noise Modeling via Machine Learning
Yanjun Ji, Marco Roth, David A. Kreplin +2
Maximizing the computational utility of near-term quantum processors requires predictive noise models that inform robust, noise-aware compilation and error mitigation. Conventional…
Quantum vs. classical: A comprehensive benchmark study for predicting time series with variational quantum machine learning
Tobias Fellner, David Kreplin, Samuel Tovey +1
Variational quantum machine learning algorithms have been proposed as promising tools for time series prediction, with the potential to handle complex sequential data more effectiv…
ML-Based Bidding Price Prediction for Pay-As-Bid Ancillary Services Markets: A Use Case in the German Control Reserve Market
Vincent Bezold, Lukas Baur, Alexander Sauer
The increasing integration of renewable energy sources has led to greater volatility and unpredictability in electricity generation, posing challenges to grid stability. Ancillary…