Publikationen der FOR 5339

Hier finden Sie die Publikationen der FOR 5339, welche mit Unterstützung der DFG im Laufe der Projektes veröffentlicht wurden.

Titel	Autor	Quelle
Towards using the Solid Protocol for Data Transport in International Data Spaces (IDS)	Christoph Braun, Yanxiu Wuwang, Zhi Wang, Xuyang Hou and Tobias Käfer	The Third International Workshop on Semantics in Dataspaces, co-located with the Extended Semantic Web Conference, June 01, 2025, Portorož, Slovenia, Towards using the Solid Protocol for Data Transport in International Data Spaces (IDS)
Towards a Generalised Information Modell: A Bayesian Network Approach for PPR-Representation	Alexander Bott, Jan Baumgärtner, Nicolaus Klein, Alexander Puchta & Jürgen Fleischer	Sustainable Manufacturing Innovations: Focus on New Energy Vehicles, Production Robots, and Software-Defined Manufacturing Proceedings of ICSM 2024, Shanghai, China, Towards a Generalised Information Modell: A Bayesian Network Approach for PPR-Representation
Multi-target regression and cross-validation for non-isothermal glass molding experiments with small sample sizes	Hendrik Mende, Saksham Kiroriwal, Julius Pfrommer, Robert H. Schmitt, Jürgen Beyerer	SPIE Optifab, 2023, Rochester, New York, United States Multi-target regression and cross-validation for non-isothermal glass molding experiments with small sample sizes
Local Calibration Testing in Supervised Machine Learning Models Using Input Space Kernels	Markus Walker, Marcel Reith-Braun, Uwe D. Hanebeck	Proceedings of the 28th International Conference on Information Fusion (FUSION 2025), pp. 1–8, Rio de Janeiro, Brazil, Local Calibration Testing in Supervised Machine Learning Models Using Input Space Kernels
Bridging Bayesian Inference and Neural Network Training: Equivalence of KBNN and Statistical Linearization	Hayk Amirkhanian, Markus Walker, Uwe D. Hanebeck, Marco F. Huber	Proceedings of the 28th International Conference on Information Fusion (FUSION 2025), pp. 1–8, Rio de Janeiro, Brazil, Bridging Bayesian Inference and Neural Network Training: Equivalence of KBNN and Statistical Linearization
Trustworthy Bayesian Perceptrons	Markus Walker, Hayk Amirkhanian, Marco F. Huber, Uwe D. Hanebeck	Proceedings of the 27th International Conference on Information Fusion (FUSION 2024), Venice, Italy, Trustworthy Bayesian Perceptrons
Progressive Bayesian Particle Flows Based on Optimal Transport Map Sequences	Uwe D. Hanebeck	Proceedings of the 26th International Conference on Information Fusion (Fusion 2023), Progressive Bayesian Particle Flows Based on Optimal Transport Map Sequences
Multi-Scale Uncertainty Calibration Testing for Bayesian Neural Networks Using Ball Trees	Markus Walker, Uwe D. Hanebeck	Proceedings of the 2024 IEEE International Conference on Multisensor Fusion and Integration (MFI 2024), Pilsen, Czechia, Multi-Scale Uncertainty Calibration Testing for Bayesian Neural Networks Using Ball Trees
Adaptive Swarm Mesh Refinement	Niklas Freymuth, Philipp Dahlinger, Tobias Würth, Simon Reisch, Luise Kärger, Gerhard Neumann	NeurIPS 2023, Adaptive Swarm Mesh Refinement
Latent Task-Specific Graph Network Simulators	Philipp Dahlinger, Niklas Freymuth, Michael Volpp, Tai Hoang, Gerhard Neumann	NeurIPS 2023 AI for Science Workshop, Latent Task-Specific Graph Network Simulators
Zeiteffiziente und datenfreie Bauteil- und Prozesssimulation mithilfe von Physics-Informed Neural Networks	Tobias Würth, Anabel Prietze, Clemens Zimmerling, Constantin Krauß, Luise Kärger	NAFEMS-Magazin 2023, 68 (4), 39., Zeiteffiziente und datenfreie Bauteil- und Prozesssimulation mithilfe von Physics-Informed Neural Networks
Physics-informed neural networks for data-free surrogate modelling and engineering optimization – An example from composite manufacturing	Tobias Würth, Constantin Krauß, Clemens Zimmerling, Luise Kärger	Materials & Design, Volume 231, July 2023, 112034 Physics-informed neural networks for data-free surrogate modelling and engineering optimization
Significance of the Material Parameters within a Three-Dimensional Solid-Shell Element for Thermoforming Simulation	Johannes Mitsch, Bastian Schäfer, Kärger Luise	Material Forming: The 28th International ESAFORM Conference on Material Forming - ESAFORM 2025, Paestum, Italy, Significance of the Material Parameters within a Three-Dimensional Solid-Shell Element for Thermoforming Simulation
Influence of gripper positions on the formation of wrinkles during the thermoforming process of thermoplastic UD-tape laminates	Georg Zeeb, Johannes Mitsch, Michael Wilhelm, Luise Kärger and Frank Henning	Material Forming. 28th International ESAFORM Conference on Material Forming, ESAFORM 2025, Influence of gripper positions on the formation of wrinkles during the thermoforming process of thermoplastic UD-tape laminates
Causal structure learning using pcmci+ and path constraints from wavelet-based soft interventions	Josephine Rehak, Alexander Falkenstein, Jürgen Beyerer	Machine Learning for Cyber-Physical Systems (ML4CPS), Causal structure learning using pcmci+ and path constraints from wavelet-based soft interventions
How to quantify the maturity of production processes	Arabizadeh, Negar, Julius Pfrommer, and Jürgen Beyerer	Machine Learning for Cyber Physical Systems: 79, How to quantify the maturity of production processes
Root cause analysis using anomaly detection and temporal informed causal graphs	Josephine Rehak , Shahenda Youssef , and Jürgen Beyerer	ML4CPS–Machine Learning for Cyber-Physical Systems, Root cause analysis using anomaly detection and temporal informed causal graphs
Metrics for the evaluation of learned causal graphs based on ground truth	Josephine Rehak, Alexander Falkenstein, Frank Doehner, and Jürgen Beyerer	ML4CPS–Machine Learning for Cyber-Physical Systems, Metrics for the evaluation of learned causal graphs based on ground truth
Regression via causally informed Neural Networks	Shahenda Youssef, Frank Doehner, Jürgen Beyerer	ML4CPS–Machine Learning for Cyber-Physical Systems, Regression via causally informed Neural Networks
Towards Representing Processes and Reasoning with Process Descriptions on the Web	Andreas Harth, Tobias Käfer, Anisa Rula, Jean-Paul Calbimonte, Eduard Kamburjan, and Martin Giese	Journal: Transactions on Graph Data and Knowledge, Volume 2, Issue 1, Seite 1-32, 2024: Towards Representing Processes and Reasoning with Process Descriptions on the Web
Considering the viscoelastic material behavior in a solid-shell element for thermoforming simulation	Johannes Mitsch, Bastian Schäfer, Jan Paul Wank, Luise Kärger	In Material Forming: ESAFORM 2024; Materials Research Forum LLC, 2024; pp 457–466, Considering the viscoelastic material behavior in a solid-shell element for thermoforming simulation
Causal Temporal Neural Networks using the Conditional Average Treatment Effect	Shahenda Youssef, Julius Pfrommer, Jürgen Beyerer	IEEE Conference on Artificial Intelligence (CAI), Causal Temporal Neural Networks using the Conditional Average Treatment Effect
Joint Parameter and State-Space Modelling of Manufacturing Processes using Gaussian Processes	Saksham Kiroriwal, Julius Pfrommer, Hendrik Mende, Robert H. Schmitt, Jurgen Beyerer	IEEE 22nd International Conference on Industrial Informatics (INDIN) (pp. 1-6), Joint Parameter and State-Space Modelling of Manufacturing Processes using Gaussian Processes
Counterfactual root cause analysis via anomaly detection and causal graphs	Josephine Rehak, Anouk Sommer, Maximilian Becker, Julius Pfrommer, Jürgen Beyerer	IEEE 21st International Conference on Industrial Informatics (INDIN), Counterfactual root cause analysis via anomaly detection and causal graphs
Grounding Graph Network Simulators using Physical Sensor Observations	Jonas Linkerhägner, Niklas Freymuth, Paul Maria Scheikl, Franziska Mathis-Ullrich, Gerhard Neumann,	ICLR 2023, Grounding Graph Network Simulators using Physical Sensor Observations
Daten-effiziente Aufklärung von kausalen Zusammenhängen in technischen Systemen durch Aktive Aufklärung und die Verwendung von Vorwissen	J. Rehak	Dissertation, Karlsruhe, Karlsruher Institut für Technologie (KIT), Daten-effiziente Aufklärung von kausalen Zusammenhängen in technischen Systemen durch Aktive Aufklärung und die Verwendung von Vorwissen
Interpolation methods for orthotropic fourth-order fiber orientation tensors in context of virtual composites manufacturing	Johannes Mitsch, Constantin Krauß, Luise Kärger	Computer Methods in Applied Mechanics and Engineering 2024, 430, Interpolation methods for orthotropic fourth-order fiber orientation tensors in context of virtual composites manufacturing
Physics-informed MeshGraphNets (PI-MGNs): Neural finite element solvers for non-stationary and nonlinear simulations on arbitrary meshes	Tobias Würth, Niklas Freymuth, Clemens Zimmerling, Gerhard Neumann, Luise Kärger	Computer Methods in Applied Mechanics and Engineering 2024, 429, Physics-informed MeshGraphNets (PI-MGNs): Neural finite element solvers for non-stationary and nonlinear simulations on arbitrary meshes
Voronoi Trust Regions for Local Calibration Testing in Supervised Machine Learning Models	Markus Walker, Philipp S. Bien, Uwe D. Hanebeck	16th Symposium Sensor Data Fusion: Trends, Solutions, and Applications, Bonn, Germany, Voronoi Trust Regions for Local Calibration Testing in Supervised Machine Learning Models