| Adaptive Swarm Mesh Refinement |
Niklas Freymuth, Philipp Dahlinger, Tobias Würth, Simon Reisch, Luise Kärger, Gerhard Neumann |
https://arxiv.org/abs/2304.00818
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| Process simulation data for thermoforming of continuous fiber-reinforced composite materials |
Johannes Mitsch, Luise Kärger |
Zenodo, Process simulation data for thermoforming of continuous fiber-reinforced composite materials |
| Context-aware Learned Mesh-based Simulation via Trajectory-Level Meta-Learning |
Philipp Dahlinger, Niklas Freymuth, Tai Hoang, Tobias Würth, Michael Volpp, Luise Kärger, Gerhard Neumann |
Transactions on Machine Learning Research, issn=2835-8856, 2026, Context-aware Learned Mesh-based Simulation via Trajectory-Level Meta-Learning |
| Diffusion-Based Hierarchical Graph Neural Networks for Simulating Nonlinear Solid Mechanics |
Tobias Würth, Niklas Freymuth, Gerhard Neumann, Luise Kärger |
The Thirty-ninth Annual Conference on Neural Information Processing Systems, 2025, Diffusion-Based Hierarchical Graph Neural Networks for Simulating Nonlinear Solid Mechanics |
| 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, |
| 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 |
Hanebeck, Uwe D. |
Proceedings of the 26th International Conference on Information Fusion (Fusion 2023) |
| 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 |
| 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 |
| Modular and over-instrumented gripper frame for the handling and thermoforming uf UD fiber-reinforced thermoplastic tape laminates |
Alexander Bott, Georg Zeeb, Alexander Puchta, Frank Henning und Jürgen Fleischer |
Modular and over-instrumented gripper frame for the handling and thermoforming uf UD fiber-reinforced thermoplastic tape laminates |
| 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
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| 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 |
| Workshop report: Learning approaches for hybrid dynamical systems |
Swantje Plambeck, Nemanja Hranisavljevic, Maximilian Schmidt, Kaja Balzereit, Aaron Bracht, Magnus Redeker, Negar Arabizadeh, Alexander Diedrich, Jens Eickmeier, Oliver Niggemann, and others |
Machine Learning for Cyber Physical Systems (2025): 91, Workshop report: Learning approaches for hybrid dynamical systems |
| How to quantify the maturity of production processes |
Arabizadeh, Negar, Julius Pfrommer, and Jürgen Beyerer |
Machine Learning for Cyber Physical Systems (2025): 79, How to quantify the maturity of production processes |
| Root cause analysis using anomaly detection and temporal informed causal graphs |
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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 |
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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
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| Evaluating the Performance of RAG Methods for Conversational AI in the Airport Domain |
Yuyang Li, Pjm Kerbusch, Rhr Pruim, Tobias Käfer |
In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track), pages 794–808, Albuquerque, New Mexico. Association for Computational Linguistics. Evaluating the Performance of RAG Methods for Conversational AI in the Airport Domain |
| 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 |
| 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
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| 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 |
| Rate-dependent 3D forming simulation of thermoplastic composite materials using visco-hyperelastic material modeling and 3D hexahedral solid-shell elements |
Johannes Mitsch, Bastian Schäfer, Luise Kärger |
Composites Part A: Applied Science and Manufacturing, Volume 200, January 2026, 109306, Rate-dependent 3D forming simulation of thermoplastic composite materials using visco-hyperelastic material modeling and 3D hexahedral solid-shell elements |
| 3D solid-shell element for macroscopic composite forming simulation enabling thickness prediction |
Bastian Schäfer, Johannes Mitsch, Luise Kärger |
Composites Part A: Applied Science and Manufacturing, Volume 199, December 2025, 109162, 3D solid-shell element for macroscopic composite forming simulation enabling thickness prediction |
| Bayesian Optimization using Partially Observable Gaussian Process Network |
Saksham Kiroriwal; Julius Pfrommer; Jürgen Beyerer |
Accepted at ASCML - International Conference on Advanced Scientific Computing & Machine Learning, 2026
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| AMBER: Adaptive Mesh Generation by Iterative Mesh Resolution Prediction |
Niklas Freymuth, Tobias Würth, Nicolas Schreiber, Balazs Gyenes, Andreas Boltres, Johannes Mitsch, Aleksandar Taranovic, Tai Hoang, Philipp Dahlinger, Philipp Becker, Luise Kärger, Gerhard Neumann |
Accepted at NeurIPS 2025, AMBER: Adaptive Mesh Generation by Iterative Mesh Resolution Prediction |
| Geometry-aware RL for Manipulation of Varying Shapes and Deformable Objects |
Tai Hoang, Huy Le, Philipp Becker, Vien Anh Ngo, Gerhard Neumann |
Accepted at ICLR 2025, Geometry-aware RL for Manipulation of Varying Shapes and Deformable Objects |
| MaNGO - Adaptable Graph Network Simulators via Meta-Learning |
Philipp Dahlinger, Tai Hoang, Denis Blessing, Niklas Freymuth, Gerhard Neumann |
Accepted NeurIPS 2025, MaNGO - Adaptable Graph Network Simulators via Meta-Learning |
| Weaknesses of the ANEES and new calibration measures for multivariate predictions |
Markus Walker, Marcel Reith-Braun, Uwe D. Hanebeck |
2025 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), College Station, TX, USA, 2025, pp. 1-8, Weaknesses of the ANEES and new calibration measures for multivariate predictions |
| 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 |
| *ARTIKEL* |
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