Abstract: Scientific machine learning is an emerging research area focused on the opportunities and challenges of machine learning in the context of complex applications across science, engineering, and medicine. The most pressing problems in these application areas have attributes that make them very different in nature to computer science applications where data-driven machine learning has found success. They are problems for which black-box methods based on data alone are not enough; rather we need a synergistic combination of modern data-driven and more classical physics-based perspectives. After framing the general challenges and opportunities, I will discuss a particular class of scientific machine learning methods that weave together the perspectives of physics-based reduced-order modeling and data-driven machine learning. The result is a powerful new class of computational capabilities that combine the predictive power, interpretability and domain knowledge of physics-based models with the flexibility and computational scalability of machine learning.
US Mountain Time
Karen Willcox (University of Texas at Austin; SFI)
Our campus is closed to the public for this event.