A multidisciplinary team of researchers from Oak Ridge National Laboratory and the University of Texas at Austin developed a new machine-learning-based reduced-order model called GrainNN to predict the grain structure that forms as a metal solidifies.
A group of ORNL researchers and collaborators have been working to develop a pipeline that simulates radiotherapy across different scales, e.g., the individual cellular scale, multicellular/tissue scale, organ scale, and whole-body scale.
A team of researchers from the Oak Ridge National Laboratory (ORNL) developed a novel architecture for a hybrid quantum-classical neural network.
Researchers from Oak Ridge National Laboratory (ORNL), in collaboration with researchers from Duke University, have developed an unsupervised machine learning method, NashAE, for effective disentanglement of latent representations.
A collaboration between scientists at Oak Ridge National Laboratory (ORNL) and University of Maryland/NIST developed a theoretical approach to combine different quantum noise reduction techniques to reduce the measurement-added noise in optomechanical s
Members and students of the Computational Urban Sciences group demonstrated a method for generating scenarios of urban neighborhood growth based on existing physical structures and placement of buildings in neighborhoods.
A multidisciplinary team of researchers from Oak Ridge National Laboratory (ORNL) developed a new online heatmap method, named hilomap, to visualize geospatial datasets as online map layers when low and high trends are equally important to map users.
A team of researchers from Oak Ridge National Laboratory (ORNL), Intel Corporation and the University of Tennessee published an innovative tool-based solution to one of the most perplexing problems facing would-be users of today’s most powerful computer
A multidisciplinary team of researchers from Virginia Polytechnic Institute and State University (Virginia Tech) and Oak Ridge National Laboratory (ORNL) propose a deep learning-based intrusion detection framework, CANShield, to detect advanced
We present a rigorous mathematical analysis of the isolation random forest algorithm for outlier detection.