Bayesian; Computational modelling; Data-driven model; Model understanding; Scientific investigation; Sources of uncertainty; Theoretical modeling; Uncertainty; Uncertainty quantifications; Physics and Astronomy (all); General Physics and Astronomy
Abstract :
[en] 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.
Disciplines :
Mathematics Physics Computer science
Author, co-author :
Gal, Yarin; Oxford Applied and Theoretical Machine Learning Group, Department of Computer Science, University of Oxford, Oxford, United Kingdom
Koumoutsakos, Petros ; Computational Science and Engineering Laboratory, School of Engineering and Applied Sciences, Harvard University, Cambridge, United States
Lanusse, Francois; CNRS, CEA Saclay, Saclay, France
Louppe, Gilles ; Université de Liège - ULiège > Département d'électricité, électronique et informatique (Institut Montefiore) > Big Data
Papadimitriou, Costas; Department of Mechanical Engineering, University of Thessaly, Volos, Greece
Language :
English
Title :
Bayesian uncertainty quantification for machine-learned models in physics
Y.G. holds a Turing Articifical Intelligence Fellowship at the Alan Turing Institute, which is supported by Engineering and Physical Sciences Research Council (EPSRC) grant reference V030302/1.
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