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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Bibliografiske detaljer

Principais autores:	Matthias Kellner, Michele Ceriotti
Format:	Artigo
Sprog:	Inglês
Udgivet:	IOP Publishing 2024-01-01
Serier:	Machine Learning: Science and Technology
Fag:	uncertainty quantification atomistic modeling ensembles error estimation molecular dynamics machine-learning potentials
Online adgang:	https://doi.org/10.1088/2632-2153/ad594a
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