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How to Make AI Faster and Smarter—With a Little Help From Physics
Rose Yu has drawn on the principles of fluid dynamics to improve deep learning systems that predict traffic, model the climate, and stabilize drones during flight.
For her graduate studies, she chose the University of Southern California (USC), partly because the same uncle—who was the only person she knew in the United States—was then working at the Jet Propulsion Laboratory in nearby Pasadena. Yu, now an associate professor at the University of California, San Diego (UCSD), is a leader in a field known as “physics-guided deep learning,” having spent years incorporating our knowledge of physics into artificial neural networks. My group has already developed algorithms that can help with individual tasks, such as weather forecasting, identifying the drivers of global temperature rise, or trying to discover causal relationships like the effects of vaccination policies on disease transmission.
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