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++.
A numerical weather forecasting model (WRF) was used to simulate 120 storms over the Alabama-Coosa-Tallapoosa (ACT) river basin to explore the effect of climate change on probable maximum precipitation (PMP).
Efforts to bring ORNL’s wireless sensor platform to market are on target and proceeding as planned.