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bayesian optimization » based optimization (Expand Search)
left optimization » lead optimization (Expand Search), yet optimization (Expand Search), led optimization (Expand Search)
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edna sampling » each sampling (Expand Search), data sampling (Expand Search)
binary model » final model (Expand Search), injury model (Expand Search), tiny model (Expand Search)
bayesian optimization » based optimization (Expand Search)
left optimization » lead optimization (Expand Search), yet optimization (Expand Search), led optimization (Expand Search)
sampling bayesian » sampling design (Expand Search), applied bayesian (Expand Search), showing bayesian (Expand Search)
edna sampling » each sampling (Expand Search), data sampling (Expand Search)
binary model » final model (Expand Search), injury model (Expand Search), tiny model (Expand Search)
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Thesis-RAMIS-Figs_Slides
Published 2024“…Importantly, this strategy locates samples adaptively on the transition between facies which improves the performance of conventional \emph{<i>MPS</i>} algorithms. In conclusion, this work shows that preferential sampling can contribute in \emph{<i>MPS</i>} even at very small sampling regimes and, as a corollary, demonstrates that prior models (obtained form a training image) can be used effectively not only to simulate non-sensed variables of the field, but to decide where to measure next.…”