Pacific Northwest National Laboratory (PNNL)
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Maintaining the Nation’s Power Grid with Exascale Computing

The ExaSGD project uses models and data analytics to understand power grid failures and look for ways to prevent them.

Optimizing the North American Power Grid to Improve Reliability and Support Grid Decarbonization

The ExaSGD project wants to enable the day-to-day operation of the national power grid to hold a large number of renewable power sources.

Applying Graph Algorithms to Solve Key Science Problems of Importance to the Nation

ExaGraph aims to leave a lasting legacy of algorithms, implementations, and graph-enabled applications for scientific discovery.

Machine Learning, Neural Networks Probe the Interactions of Water Molecules

An article by Pacific Northwest National Laboratories on Technology.org shares how machine learning algorithms, the basis of neural networks, are bringing new scientific discoveries closer to reality.

Delivering Exascale Machine Learning Algorithms and Tools for Scientific Research

Machine learning, artificial intelligence, and data analytics are converging with high-performance computing to advance scientific discovery.

Rewriting a Legacy Computational Chemistry Software Package for Larger Simulations and Exascale Speed

A collaborative team is working to get NWChem ready to run on exascale machines and to provide a starting point for future code development.

ECP Announces New Co-Design Center to Focus on Exascale Machine Learning Technologies

New ExaLearn Co-Design Center to be led by Brookhaven National Laboratory's Francis (Frank) Alexander. The Exascale Computing Project has initiated its sixth Co-Design Center, ExaLearn, to be led by Principal

ModSim 2017: Guiding the Future of Systems and Applications and Enriching the HPC Community

Paul Messina, director of the Exascale Computing Project, on August 9 delivered the keynote presentation at the Workshop on Modeling & Simulation of Systems, ModSim 2017, in Seattle, Washington.

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