A climate model combining generative AI and physics data is 25 times faster than the state of the art.
A climate model combining generative AI and physics data is 25 times faster than the state of the art.
The algorithms behind generative AI tools like DallE, when combined with physics-based data, can be used to develop better ways to model the Earth’s climate. Computer scientists in Seattle and San Diego have now used this combination to create a model that is capable of predicting climate patterns over 100 years 25 times faster than the state of the art.
Specifically, the model, called Spherical DYffusion, can project 100 years of climate patterns in 25 hours–a simulation that would take weeks for other models. In addition, existing state-of-the-art models need to run on supercomputers. This model can run on GPU clusters in a research lab.
“Data-driven deep learning models are on the verge of transforming global weather and climate modeling,” the researchers from the University of California San Diego and the Allen Institute for AI, write.
Read more at University of California - San Diego
Image: From left: Rose Yu, a faculty member in the UC San Diego Department of Computer Science and Engineering, and Ph.D. student Salva Ruhling Cachay examine data. (Credit: David Baillot/University of California San Diego)
