Tackling climate change with machine learning
kEnergy
Scientific machine learning for wind, solar resources, emissions and the energy transition.
Enhancing Wind Downscaling with Foundation Models
Site-scale methane plume simulation and validation from oil and gas facilities through advanced dispersion, atmospheric modeling, and scientific machine learning
A 3D super-resolution of wind fields via physics-informed pixel-wise self-attention generative adversarial network
High-Resolution WRF Wind Field Dataset for GHG Applications: Navigating Computational Challenges in Energy Research
Mesoscale simulations of Australian direct normal irradiance, featuring an extreme dust event
Towards a More Accurate Machine Learning Multi-Model Ensemble Method for Direct Solar Irradiance Forecasts
Drawdown: The most comprehensive plan ever proposed to reverse global warming
Talk
How Can AI Accelerate the Global Energy Transition? — S. Karthik Mukkavilli