PNNL data scientists Henry Kvinge and Ted Fujimoto presented their research on few-shot learning and reinforcement learning, respectively, at workshops during the 2021 AAAI Conference on Artificial Intelligence.

PNNL-developed Water Balance Tool estimates consumption for major water end-uses. Understanding the breakout of water use identifies water efficiency opportunities and allows facility managers to spot potential system losses.

New approach allows nonexperts to optimize control of a building's energy systems without adding computing power or proprietary software.

PNNL created an assessment method and maturity model that helps manufacturers building products for the power grid implement consistent cybersecurity best practices throughout their development lifecycle.

Ann Lesperance, national security advisor, joins the National Academies of Sciences, Engineering, and Medicine Committee on Applied Research Topics for Hazard Mitigation and Resilience.

The partnership to apply artificial intelligence to improve complex systems is part of a U.S. Department of Energy Office of Science $4.2 million, three-year grant.

PNNL scientists joined international leaders in artificial intelligence research to discuss the latest advances, opportunities, and challenges for neural information processing—the foundation for AI.

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.

Buildings account for around 40 percent of our nation's energy use and consume 75 percent of our nation’s electricity each year. Energy use is also one of the biggest costs for facility owners.

Data Scientist Sam Chatterjee to chair security and defense specialty group of the Society for Risk Analysis.

Researchers at PNNL revisit a disaster recovery framework for relevance to COVID-19 pandemic.

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

PNNL computational biologists, structural biologists, and analytical chemists are using their expertise to safely accelerate the design step of the COVID-19 drug discovery process.

David Manz is a cybersecurity researcher who is working with cyber-physical systems to better protect the critical infrastructure.

Using public data from the entire 1,500-square-mile Los Angeles metropolitan area, PNNL researchers reduced the time needed to create a traffic congestion model by an order of magnitude, from hours to minutes.

PNNL researchers have shown an improved binarized neural network can deliver a low-cost and low-energy computation to help the performance of smart devices and the power grid.

PNNL’s longstanding grid and buildings capabilities are driving two projects that test transactive energy concepts on a grand scale and lay the groundwork for a more efficient U.S. energy system.

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.

PNNL data scientists Ján Drgoňa and Draguna Vrabie are working to reduce deployment costs of advanced energy control systems for buildings.

Researchers developed a new tool that combines artificial intelligence and microscopy data to rapidly test new materials for fusion reactor use.