Bayesian uncertainty quantification for machine-learned models in physics
Date
2022Language
en
Sujet
Résumé
Being able to quantify uncertainty when comparing a theoretical or computational model to observations is critical to conducting a sound scientific investigation. With the rise of data-driven modelling, understanding various sources of uncertainty and developing methods to estimate them has gained renewed attention. Five researchers discuss uncertainty quantification in machine-learned models with an emphasis on issues relevant to physics problems. © 2022, Springer Nature Limited.
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