Physical rules govern much of our world. Gravity is one. E = mc2 is another. And Newton’s three laws of motion explain why, for example, an object at rest tends to stay put.
For years, physicists have programmed these rules into classic computer simulations to explore phenomena such as weather or galaxy formation. But these simulations require a lot of manual work to build.
Deep learning could help. Models trained on reams of data can quickly spot trends or relationships all on their own. But they often return results that violate the laws of physics.
Rose Yu, 34, is a leader in physics-guided deep learning, an emerging field that attempts to bake real-world rules into AI systems. She works with scientists to understand the physical laws most relevant to their research. Then she develops models that obey those laws, meaning they only produce scenarios that could happen in the real world. And she trains those models on large sets of relevant data.
Her methods have led to many real-world advances. As a postdoc at Caltech, she built a model to create more accurate traffic forecasts for Los Angeles; Alphabet later deployed it in Google Maps. During the pandemic, she co-led a team to project US covid deaths; the US Centers for Disease Control and Prevention then incorporated that work into its own algorithms.
Recently, Yu worked with collaborators to improve the resolution of climate models. Her algorithms are especially adept at describing turbulence—critical to understanding hurricanes or El Niño. She has sped simulations of that phenomenon by three orders of magnitude, she says. Now she’s partnering with the fusion company General Atomics and others on a three-year project to model how plasma interacts with the inside of a nuclear reactor.
As her projects grow in scope, Yu faces some increasingly familiar challenges. Deep learning requires a lot of training data and computing power. And it’s difficult to prove that any AI model trained on a limited data set will generate accurate answers when it tackles new problems.
For now, Yu trains different models for each domain she works in. Someday, she’d like to combine them into one model that could answer many different types of questions. Such a system may even help scientists discover new physics by uncovering patterns that would otherwise be hard to spot.
