Accelerating high-throughput simulations using machine learning methods
Recorded 29 May 2017 in Lausanne, Vaud, Switzerland
Modern machine learning techniques allows one to fit (non-linear) models with many unknown parameters while maintaining control of issues relating to overfitting. When moving towards massively high-throughput simulations many parts of the simulations can benefit from being integrated with machine learning. Examples relate to improved initial guesses, faster search algorithms using surrogate machine learning models, systematic error estimation, reduction of model complexity, and improved simulation model accuracy.
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