About This Course
This training covers machine learning applied to subsurface uncertainty quantification. It works through the uncertainty problem and why it is computationally hard, supervised learning fundamentals, surrogate and proxy models, experimental design for training sets, uncertainty in the predictions themselves, Bayesian methods and inversion, ensemble and history matching applications, validation requirements and the recognition of models that have learned the wrong thing.
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