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DOE OSTI · 1670557

Uncertainty Quantification in Scientific ML

Thiagarajan, Jayaraman J.·Anirudh, Rushil·Bremer, Peer-Timo·Venkatesh, Bindya
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Abstract

The intricate interactions between data sampling, model selection and the inherent randomness in complex systems strongly emphasize the need for a rigorous characterization of ML algorithms. In conventional statistics, uncertainty quantification (UQ) provides this characterization by measuring how accurately a model reflects the physical reality and by studying the impact of different error sources on the prediction.

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Thiagarajan, Jayaraman J., Anirudh, Rushil, Bremer, Peer-Timo, Venkatesh, Bindya. 2020-10-02. Uncertainty Quantification in Scientific ML. https://doi.org/10.2172/1670557

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