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Bayesian uncertainty quantification for machine-learned models in physics

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. Yarin Gal and four other experts discuss uncertainty quantification in machine-learned models with an emphasis on issues relevant to physics problems.

Yarin Gal, Petros Koumoutsakos, Francois Lanusse, Gilles Louppe, Costas Papadimitriou
Nature Reviews Physics volume 4, pages 573–577 (2022)
[Nature Review Physics]

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