A team of researchers from Oak Ridge National Laboratory demonstrated highly scalable performance across thousands of GPUs in a newly released version of the open-source MEUMAPPS phase-field simulation framework.
Filter Research Highlights
Area of Research
- (-) Computer Science (52)
- Advanced Manufacturing (1)
- Building Technologies (1)
- Clean Energy (3)
- Climate and Environmental Systems (3)
- Computational Biology (1)
- Computational Engineering (8)
- Data (1)
- Energy Sciences (3)
- Geographic Information Science and Technology (1)
- Materials (7)
- Materials for Computing (3)
- Mathematics (4)
- Quantum information Science (10)
- Renewable Energy (2)
- Sensors and Controls (1)
- Supercomputing (9)
This work develops an approach for engineering non-Gaussian photonic states in discrete frequency bins.
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.
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 Oak Ridge National Laboratory (ORNL) have developed a distributed implementation of graph convolutional neural networks [1].
ORNL researchers developed a novel nonlinear level set learning method to reduce dimensionality in high-dimensional function approximation.
Researchers at ORNL have created a unique simulation technology that allows software systems to participate in slower than real time simulation exercises, and to accomplish this without requiring recompilation of source code, relinking of object files,
Researchers from Oak Ridge National Laboratory (ORNL) demonstrated that mode connectivity exists in the loss landscape of parameterized quantum circuits.