PNNL

Disturbance Propagation Stability in Droop-Controlled Microgrids

Estuarine nutrient pollution impact reduction assessment through euphotic zone avoidance/bypass considerations

Energy Storage in Local Zoning Ordinances

A Self-Sustained CPS Design for Reliable Wildfire Monitoring

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

Evaluation of Liquid Cloud Albedo Susceptibility in E3SM Using Coupled Eastern North Atlantic Surface and Satellite Retrievals

Pyflowline: a mesh independent river network generator for hydrologic models

Watershed-Based Representation Discretized into Topographic Units Better Captures Land Surface Heterogeneity

Watershed-based representation discretized into topographic subgrid units better captured small-scale land cover, precipitation, temperature, and snow variability across multiple spatial scales.

Machine Learning Improves Compound Flood Simulation in Earth System Models

Physics-informed and data-driven neural network models enhance Earth system models in simulating compound flooding at river-ocean interfaces.

Autonomous Control for Cyber Enabled Infrastructure

Pacific Northwest National Laboratory (PNNL) is advancing autonomous learning technologies to bolster U.S. critical infrastructure resilience.