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.
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].
Simulations of Inconel 625 microstructure development and constitutive properties during Selective Laser Melting processing were performed utilizing two exascale-capable codes on the pre-exascale Summit supercomputer.
Kokkos is a programming model and library for writing performance-portable code in C++.