NDNET: A Unified Framework for Anomaly and Novelty Detection. - Jens Decke; Jörn Schmeißing; Diego Botache; Maarten Bieshaar; Bernhard Sick; Christian Gruhl (ARCS, 2022)

DADO - Low-Cost Query Strategies for Deep Active Design Optimization. - Jens Decke; Christian Gruhl; Lukas Rauch; Bernhard Sick (ICMLA, 2023)

Dataset of a parameterized U-bend flow for Deep Learning Applications. - Jens Decke; Olaf Wünsch; Bernhard Sick (CoRR, 2023)

DADO - Low-Cost Selection Strategies for Deep Active Design Optimization. - Jens Decke; Christian Gruhl; Lukas Rauch; Bernhard Sick (CoRR, 2023)

Enhancing Multi-Objective Optimization through Machine Learning-Supported Multiphysics Simulation. - Diego Botache; Jens Decke; Winfried Ripken; Abhinay Dornipati; Franz Götz-Hahn; Mohamed Ayeb; Bernhard Sick (CoRR, 2023)

Self-Aware Control for Autonomous Underwater Vehicles. - Jens Decke; Florian Gerland; Thomas Schomberg; Olaf Wünsch; Bernhard Sick; Christian Gruhl (ACSOS-C, 2024)

An Efficient Multi Quantile Regression Network with Ad Hoc Prevention of Quantile Crossing. - Jens Decke; Arne Jenß; Bernhard Sick; Christian Gruhl (ARCS, 2024)

From Structured to Unstructured: A Comparative Analysis of Computer Vision and Graph Models in Solving Mesh-Based PDEs. - Jens Decke; Olaf Wünsch; Bernhard Sick; Christian Gruhl (ARCS, 2024)

Active Learning with Physics-Informed Graph Neural Networks on Unstructured Meshes. - Jens Decke; Alexander Heinen; Bernhard Sick; Christian Gruhl (IAL@PKDD/ECML, 2024)

Towards Deep Active Learning in Avian Bioacoustics. - Lukas Rauch; Denis Huseljic; Moritz Wirth; Jens Decke; Bernhard Sick; Christoph Scholz 001 (IAL@PKDD/ECML, 2024)

Enhancing Multi-objective Optimisation Through Machine Learning-Supported Multiphysics Simulation. - Diego Botache; Jens Decke; Winfried Ripken; Abhinay Dornipati; Franz Götz-Hahn; Mohamed Ayeb; Bernhard Sick (ECML/PKDD (10), 2024)