PNNL

The Buoy is Back: What Data Can Tell Us About Offshore Wind

Flexible Reinforcement Learning Framework for Building Control using EnergyPlus-Modelica Energy Models

Machine Learning to Automate Red Teaming for Cyber-Physical Systems

Red teaming for CPS, the process of challenging systems, involves a group of cybersecurity experts to emulate end-to-end cyberattacks following a set of realistic tactics, techniques, and procedures.

PNNL @ NeurIPS 2020

PNNL data scientists and engineers will be presenting at NeurIPS, the Thirty Fourth Conference on Neural Information Processing Systems, and the co-located Women in Machine Learning workshop, WiML.

National Laboratory Pulls Together to Address the COVID-19 Pandemic

PNNL has increased the nation’s capacity to test for COVID-19, demonstrating that reagents and equipment from additional manufacturers meet the standards needed to yield accurate test results.

Sequence-to-sequence neural networks for short-term electrical load forecasting in commercial office buildings

Water Research Earns INCITE Supercomputer Access

The project received an Innovative and Novel Computational Impact on Theory and Experiment (INCITE) award, a highly competitive U.S. Department of Energy Office of Science program.

Physicist Malachi Schram Co-Edits ‘AI for Nuclear Physics’ Report

The workshop brought together 184 scientists to explore research opportunities for nuclear physics in artificial intelligence.

Decoding Protein Interactions with Domain-Aware Machine Learning

Parallel graph neural networks identify protein-ligand 3-D structural interactions to aid in protein function prediction and drug discovery.

A New Look at One of the Most Abundant Particles in the Universe

Researchers at PNNL are applying deep learning techniques to learn more about neutrinos, part of a worldwide network of researchers trying to understand one of the universe’s most elusive particles.