Machine Learning to Discover Physics and Engineering Principles with Nathan Kutz

In this episode, I'm joined by Nathan Kutz, Professor of applied mathematics, electrical engineering and physics at the University of Washington. Nathan and I met a few months ago at the Prepare.AI conference in St. Louis where he gave a talk on "Machine Learning to Discover Physics and Engineering Principles." Our conversation is laser-focused on his research into the use of machine learning to help discover the fundamental governing equations for physical and engineering systems from time series measurements. We explore the application of his work to self-tuning fiber-optic lasers as well as to biological systems and other complex multi-scale systems. On July 17th at 5pm PT, Nic Teague will lead a discussion on the paper Quantum Machine Learning by Jacob Biamonte et al, which explores how to devise and implement concrete quantum software for accomplishing machine learning tasks.