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A posteriori error estimator based on gradient recovery by averaging for discontinuous Galerkin methods.
Emmanuel Creusé
Serge Nicaise
Published in:
J. Comput. Appl. Math. (2010)
Keyphrases
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significant improvement
maximum likelihood
empirical studies
gradient method
data sets
neural network
multiscale
computational cost
probabilistic model
benchmark datasets
machine learning methods
qualitative and quantitative
density estimation
error detection
steepest ascent