PNNL’s Center for Cloud Computing is addressing challenges in data management and hybrid cloud solutions with industry and academic partnerships.

PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.

In a recent publication in Nature Communications, a team of researchers presents a mathematical theory to address the challenge of barren plateaus in quantum machine learning.

A PNNL study developed a water management module for Xanthos that distinguishes between the operational characteristics of hydropower, irrigation, and flood control reservoirs.

With the help of the U.S. State Department, PNNL climate scientists assisted counterparts in Malaysia and Thailand in exploring carbon neutral goals.

PNNL energy storage experts express need for continued investment in developing and deploying long-duration energy storage.

PNNL researchers outline open problems in different scientific areas in the Journal of Combinatorics.

Report for the Oregon Public Utility Commission highlights innovations and best practices for resilience and utility planning could be helpful to other states as well.

HiSVSIM enables large-scale quantum simulations through graph partitioning.

New work developed a methodology to generate building temperature setpoint schedules for use by modelers in large-scale building energy simulations.