ORNL researchers developed a novel nonlinear level set learning method to reduce dimensionality in high-dimensional function approximation.
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ORNL researchers developed a stochastic approximate gradient ascent method to reduce posterior uncertainty in Bayesian experimental design involving implicit models.
A team of ORNL researchers has used the DCA++ application, a popular code for predicting the performance of quantum materials, to verify two performance-enhancing strategies.
Kokkos is a programming model and library for writing performance-portable code in C++.
Dendritic solidification and microstructure evolution play a vital role in determining the material properties. Capturing the morphology of the solidification front becomes critical in predicting the final dendritic structure.
This work studies deformation of EV battery module under external mechanical loading and effect of inactive module components on module failure.