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![ExaSGD: Using High-Performance Computing to Operate Decarbonized Resilient Grid CSED ORNL Computational Sciences and Engineering](/sites/default/files/styles/list_page_thumbnail/public/2022-06/exasgd-_using_high-performance_computing_to_operate_decarbonized_resilient_grid.png?h=3c43bafe&itok=hvp7AK3a)
As the growth of data sizes continues to outpace computational resources, there is a pressing need for data reduction techniques that can significantly reduce the amount of data and quantify the error incurred in compression.
![Variational Generative Flows for Reconstruction Uncertainty Estimation CSMD Computer Science and Mathematics Division ORNL](/sites/default/files/styles/list_page_thumbnail/public/2022-07/variational_generative_flows_for_reconstruction_uncertainty_estimation.png?h=429f3186&itok=Hhws1ahJ)
A research team from ORNL and Pacific Northwest National Laboratory has developed a deep variational framework to learn an approximate posterior for uncertainty quantification.