As the world’s climate changes, extreme events considered “once-in-a-century” emergencies appear much more frequently than the name suggests. From prolonged cold snaps and heat waves to infrastructure collapses such as floods and blackouts, these rare and hard-to-predict events challenge governments and industries in their preparation for worst case scenarios. But a new collaboration between University of Chicago and Argonne National Laboratory researchers will apply artificial intelligence to accelerate the scientific simulation of complex physical systems, with the potential to more accurately determine the probability of these extremes.
The project, funded through a $3.25 million grant from the U.S. Department of Energy (DOE), will explore the fundamentals of “surrogate models” — simplified models built using artificial intelligence that speed up the complex scientific models for climate, energy infrastructure, and other systems. By allowing researchers to run many more simulations in the same amount of time, these surrogates enable better quantification of the risk of extreme events, the use of computer modeling in rapid decision-making, and other advantages.
But questions remain about whether these new models adequately represent the original versions, which solve complicated mathematical equations to recreate physical processes. The project will also draw from other AI applications to find new ways of creating and training surrogate models.
“We will systematically explore different families of models and try to understand which characteristics are most predictive of whether or not we'll be able to build a good surrogate,” said Rebecca Willett, professor of statistics and computer science at the University of Chicago and principal investigator on the new project. “We're also taking ideas that have been developed and explored in the context of natural language processing or computer vision and incorporating those ideas into surrogates, hopefully by incorporating additional physics information to make them more robust to small amounts of data or extreme events.”
Joining Willett in this collaboration are assistant professors Yuehaw Khoo and Daniel Sanz-Alonso of the UChicago Department of Statistics, assistant professor of mathematics Dana Mendelson, Argonne Assistant Computational Statistician Julie Bessac, and Mihai Anitescu, Senior Computational Mathematician at Argonne and part-time professor of statistics at UChicago. Willett, Anitescu, Khoo, and Sanz-Alonso are also part of the Committee on Computational and Applied Mathematics (CCAM). The award was part of $16 million awarded by the DOE to five groups studying data-intensive scientific machine learning and analysis.
“The University of Chicago is committed to working with our national laboratory partners on large-scale problems like climate change, which can only be addressed through these types of scientific collaborations across disciplines and institutions,” said Juan de Pablo, UChicago Vice President for National Laboratories, Science Strategy, Innovation, and Global Initiatives. “AI technology can play a key role in addressing challenging issues that have global impact.”