Skip to content
Menu
Technical University of Munich
Technical University of Munich

Bayesian Modeling

Quantifiying the uncertanty of molecular models and the posterior robust prediction of simulation results.
Recent Papers:

Scalable Bayesian Uncertainty Quantification for Neural Network Potentials: Promise and Pitfalls

S. Thaler, G. Doehner, J. Zavadlav, J. Chem. Theory Comput. 2023, paper, arXiv

Uncertainty Quantification of out-of-distribution properties with scalable Bayesian methods applicable to Graph Neural Network potentials.

Bayesian selection of coarse-grained models of liquid water

J. Zavadlav, G. Arampatzis, P. Koumoutsakos, Sci. Rep. 2019, paper,  arXiv

A data driven evaluation and selection for CG water models through a Hierarchical Bayesian framework.

To top