Seeking a deeper understanding of when, where, and why large wildfires occur using explainable artificial intelligence.

Researchers used deep learning methods to estimate the subsurface permeability of a watershed from stream discharge measurements.

Rigorous mathematical methods can help avoid unnecessary simplifications in numerical models of atmospheric clouds.

More accurate methods provide better results in numerical simulations of atmospheric clouds by avoiding accidental contradictions.

Microbiome and soil chemistry characterization at long-term bioenergy research sites challenges idea that switchgrass increases carbon accrual in surface soils of marginal lands.

Researchers developed a new tool that combines artificial intelligence and microscopy data to rapidly test new materials for fusion reactor use.

Contributions from researchers across Pacific Northwest National Laboratory (PNNL) were recently recognized in the preliminary findings of a Secretary of Energy Advisory Board (SEAB) report.