Speaker
Schmerler, Steve
(HZDR)
Description
This talk gives a brief introduction to uncertainty quantification (UQ) for neural networks. We investigate these methods as part of a Helmholtz AI voucher in collaboration with the MALA [1,2] project, where we build surrogate models to speed up demanding density functional theory calculations. In this context, UQ methods can be used to asses the validity of model predictions and can also serve to detect out-of-distribution data.
[1] https://github.com/mala-project/mala
[2] J. A. Ellis et al., Phys. Rev. B 104, 035120, 2021
| Physical Presentation | I would not feel comfortable to present in front of an audience and prefer a video (call) presentation. |
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Primary authors
Schmerler, Steve
(HZDR)
Mr
Hanumant Kulkarni, Somashekhar
(CASUS)
Cangi, Attila
(Center for Advanced Systems Understanding, HZDR)
Fiedler, Lenz
(Center for Advanced Systems Understanding, HZDR)
