2 citations · 4 across the 6 of their papers we have counts for
3 papers · 1 filter
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…
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…
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…