Uncertainty quantification by direct propagation of shallow ensembles

Uncertainty quantification by direct propagation of shallow ensembles

Statistical learning algorithms provide a generally-applicable framework to sidestep time-consuming experiments, or accurate physics-based modeling, but they introduce a further source of error on top of the intrinsic limitations of the experimental or theoretical setup. Uncertainty estimation is es...

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Bibliographic Details
Main Authors: Matthias Kellner, Michele Ceriotti
Format: Article
Language:English
Published: IOP Publishing 2024-01-01
Series:Machine Learning: Science and Technology
Subjects:
uncertainty quantification
atomistic modeling
ensembles
error estimation
molecular dynamics
machine-learning potentials
Online Access:https://doi.org/10.1088/2632-2153/ad594a

Internet

https://doi.org/10.1088/2632-2153/ad594a

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