The use of disciplines in pure mathematics can increase the reliability and explainability of machine learning models that “transcend human intuition,” according to PNNL scientists.
To overcome high-performance computing bottlenecks, a research team at PNNL proposed using graph theory, a mathematical field that explores relationships and connections between a number, or cluster, of points in a space.
Scientists are pioneering approaches in the branch of artificial intelligence known as machine learning to design and train computer software programs that guide the development of new manufacturing processes.
A new testbed facility capable of testing superconducting qubit fidelity in a controlled environment free of stray background radiation will benefit quantum information sciences and the development of quantum computing.
Tiffany Kaspar’s work has advanced the discovery and understanding of oxide materials, helping develop electronics, quantum computing, and energy production. She strives to communicate her science to the public.
Scott Chambers creates layered structures of thin metal oxide films and studies their properties, creating materials not found in nature. He will soon move his instrumentation and research to the new Energy Sciences Center.
A Q&A with Lauren Charles, veterinarian and PNNL data scientist, on zoonotic diseases and the role biosurveillance plays in mitigating the growing threat to global health.
PNNL’s new Hydrogen Energy Storage Evaluation Tool allows users to examine multiple energy delivery pathways and grid applications to maximize benefits.
PNNL combines AI and cloud computing with damage assessment tool to predict path of wildfires and quickly evaluate the impact of natural disasters, giving first responders an upper hand.