A standard problem in uncertainty quantification and in computational statistics is the sampling of stationary Gaussian random fields with given covariance in a d-dimensional (physical) domain. In ...
In this research field we are developing advanced computational methods centered around efficient solution strategies for partial differential equations. In numerical analysis, we focus on developing ...
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Deep learning method enables efficient Boltzmann distribution sampling across a continuous temperature range
A research team has developed a novel direct sampling method based on deep generative models. Their method enables efficient sampling of the Boltzmann distribution across a continuous temperature ...
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