PNNL’s Center for Cloud Computing is addressing challenges in data management and hybrid cloud solutions with industry and academic partnerships.

New datasets delineating global urban land support scientific research, application, and policy, but they can produce different results when applied to the same problem making it difficult for researchers to decide which to use.

The demand for energy is growing—and so is the technology supporting it. However, future development of power generation technologies could be affected by a key factor: material supply.

In a recent publication in Nature Communications, a team of researchers presents a mathematical theory to address the challenge of barren plateaus in quantum machine learning.

The Delaware River Basin is projected to experience a regional increase in flood severity under future climate scenarios.

Pacific Northwest may face increase in summer heat-dome-like stationary waves.

Earth Scientist Laura Fierce mentors Department of Energy Science Graduate Student Research program awardees.

Researchers created a methodology to design custom network topologies that improve the execution time for scientific applications.

Variations in the level of market globalization can greatly affect the amount of water required to meet future global demand for agricultural commodities.

Climate change and socioeconomic pressures are transforming passenger and freight transportation in the Arctic, producing effects that have yet to be fully understood.

Testing the assumption that different future socio-economic development patterns, which result in different land-use changes, can be paired with different future climate outcomes for risk assessments in a multi-model framework.

Incorporating spatially explicit land characteristics in a global model illustrates the complex effects of applying uniform regional protection assumptions in a global analysis.

Researchers unveil user-friendly software to keep complex projects on budget and on schedule.

PNNL researchers present challenges and opportunities for fusing graph neural networks with deep reinforcement learning.

Unveiling diverse scenarios and implications for water resource management in a rapidly changing world.

Quantifying the long-term destination of anthropogenic emissions and robustness of global carbon sinks.

New study identifies crucial factors for more accurate hydrological predictions in the Community Land Model version 5.

Groundwater modeling of irrigation return flow dynamics is important for improving estimates of water availability.

PNNL researchers outline open problems in different scientific areas in the Journal of Combinatorics.

Methods review suggests ways to use artificial intelligence and machine learning to handle missing data when integrating two or more omics approaches.