2 citations · 4 across the 6 of their papers we have counts for
6 papers
A Continuous-Time Consistency Model for 3D Point Cloud Generation
Sebastian Eilermann, René Heesch, Oliver Niggemann
Fast and accurate 3D shape generation from point clouds is essential for applications in robotics, AR/VR, and digital content creation. We introduce ConTiCoM-3D, a continuous-time…
On-the-Fly Fine-Tuning of Foundational Neural Network Potentials: A Bayesian Neural Network Approach
Tim Rensmeyer, Denis Kramer, Oliver Niggemann
Due to the computational complexity of evaluating interatomic forces from first principles, the creation of interatomic machine learning force fields has become a highly active fie…
Evaluating Large Language Models for Real-World Engineering Tasks
Rene Heesch, Sebastian Eilermann, Alexander Windmann +3
Large Language Models (LLMs) are transformative not only for daily activities but also for engineering tasks. However, current evaluations of LLMs in engineering exhibit two critic…
On the Convergence of Locally Adaptive and Scalable Diffusion-Based Sampling Methods for Deep Bayesian Neural Network Posteriors
Tim Rensmeyer, Oliver Niggemann
Achieving robust uncertainty quantification for deep neural networks represents an important requirement in many real-world applications of deep learning such as medical imaging wh…
Position Paper on Materials Design -- A Modern Approach
Willi Grossmann, Sebastian Eilermann, Tim Rensmeyer +4
Traditional design cycles for new materials and assemblies have two fundamental drawbacks. The underlying physical relationships are often too complex to be precisely calculated an…
A Generative Neural Network Approach for 3D Multi-Criteria Design Generation and Optimization of an Engine Mount for an Unmanned Air Vehicle
Christoph Petroll, Sebastian Eilermann, Philipp Hoefer +1
One of the most promising developments in computer vision in recent years is the use of generative neural networks for functionality condition-based 3D design reconstruction and ge…