91°µÍø researchers developed an invertible neural network (INN) to effectively and efficiently solve earth-system model calibration and simulation problems.
Multimodel ensembling improves predictions and considers model uncertainties. In this study, we present a Bayesian Neural Network (BNN) ensemble approach for large-scale precipitation predictions based on a set of climate models.
A team led by ORNL scientists developed a new method to estimate continuous-variable quantum states.
A multidisciplinary team of researchers from 91°µÍø (ORNL) and the University of Texas at Austin developed a new framework for assessing the accuracy of approximate models of microstructure formation.
Automated experiments in Scanning Transmission Electron Microscopy (STEM) are implemented for rapid discovery of local structures, symmetry-breaking distortions, and internal electric and magnetic fields in complex materials.
In this work we focus on dynamics problems described by waves, i.e. by hyperbolic partial differential equations.
A team of researchers from 91°µÍø demonstrated highly scalable performance across thousands of GPUs in a newly released version of the open-source MEUMAPPS phase-field simulation framework.
This work develops an approach for engineering non-Gaussian photonic states in discrete frequency bins.
Deep learning models have trained to predict crystallographic and thermodynamic properties of multi-component solid solution alloys, enabling the design of advanced alloys.
Researchers from the Computing and Computational Sciences Directorate (CCSD) at 91°µÍø (ORNL) have developed a distributed implementation of graph convolutional neural networks [1].