2 citations · 4 across the 4 of their papers we have counts for
4 papers
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…
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…
High Accuracy Uncertainty-Aware Interatomic Force Modeling with Equivariant Bayesian Neural Networks
Tim Rensmeyer, Benjamin Craig, Denis Kramer +1
Even though Bayesian neural networks offer a promising framework for modeling uncertainty, active learning and incorporating prior physical knowledge, few applications of them can…