PNNL

Early Exploration of a Flexible Framework for Efficient Quantum Linear Solvers in Power Systems

Evaluating Performance of Different Generative Adversarial Networks for Large-Scale Building Power Demand Prediction

Reinforcement Learning-based Output Structured Feedback for Distributed Multi-Area Power System Frequency Control

DS-GL: Advancing Graph Learning via Harnessing the Power of Nature within Dynamic Systems

Extending Power of Nature from Binary Problems to Real-Valued Graph Learning in Real World

Nature-GL: A Revolutionary Learning Paradigm Unleashing Nature’s Power in Real-World Spatial-Temporal Graph Learning

Handling and Application of Big Data in Modern Power Systems for Planning, Operation, and Control Processes

Advancing Cyber-Attack Detection in Power Systems: A Comparative Study of Machine Learning and Graph Neural Network Approaches

New AI Agent Connects Computer Reasoning with Chemistry

CACTUS provides AI-powered cheminformatics for autonomous science.

PNNL Teams Up with Fervo Energy and NVIDIA to Accelerate Geothermal Energy Development

PNNL will build an AI-powered modeling platform to help geothermal energy operators maximize electricity generation.