Harnessing machine learning to enhance plant coexistence in a vegetation demographic model.

Researchers from Pacific Northwest National Laboratory created and embedded a physics-informed deep neural network that can learn as it processes data.

The Human Factors Symposium took place at Discovery Hall at PNNL in May 2023. Fifty-seven attendees participated in the three-day event representing 15 different institutions.

Different sources of low-level moisture perturbation can result in differences in the timing and location of a large, modeled storm.

PNNL energy storage experts express need for continued investment in developing and deploying long-duration energy storage.

A holistic evaluation of the uncertainty sources that arise from configuring the model to processing the output for global variable-resolution models.

Randomly constructed neural networks can learn how to represent light interacting with atmospheric aerosols accurately at a low computational cost and improve climate modeling capabilities.

A success story of applying convergence testing to detect and address issues of numerical discretization in nonlinear representations of turbulence and clouds.

Researchers found a 150-day period in Antarctic circulation, something that many current climate models do not represent accurately.

A new open-source feature tracking package is now available to facilitate advanced model evaluation, model development efforts, and scientific discovery.

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

PNNL welcomes six Department of Energy Office of Science Graduate Student Research awardees from across the nation.

Models show that in a warmer climate, the 2012 North American derecho would shrink and decay earlier in Mid-Atlantic coastal areas.

Developed a data-assimilation method based on physics-informed deep learning to resolve downscaled flow fields within a large-scale river model.

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

Computational chemistry libraries from NWChem, NWChemEX, SPEC, and more are now available on Azure Quantum Elements.

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.

The complex interactions between the land and atmosphere can affect cloud growth.