Researchers at PNNL have developed an interpretable, lightweight AI model that can easily predict weld microstructure features using only basic machine sensor inputs.

Researchers at PNNL advised elementary and middle school student teams with their problem-solving research for the FIRST® LEGO® League robotics competitions.

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

New apps help researchers with statistical analyses of data.

A team of researchers from PNNL provided technical knowledge and support to test a suite of techniques that detect genetically modified bacteria, viruses, and cells.

PNNL signs memorandum of understanding with Oregon State University to support marine science and technology advancements.

PNNL researchers teamed up with marine energy developers to test a new wave energy conversion device through the TEAMER program.

PNNL researchers combined deep operator networks with spectral methods to efficiently and accurately solve complex equations.