22 February 2019
John Dabiri
Departments of Civil & Environmental Engineering and Mechanical Engineering
Stanford University
Our ability to predict important phenomena such as ocean climate change, cardiovascular health, or the performance of a jet engine requires a shared set of mathematical tools to describe complex fluid dynamics. In practice, we are often faced with a Goldilocks problem: we have either too much data arising from observations of those flows or too little data. In this talk we will explore new tools that have allowed us to navigate both data extremes and ultimately to optimize important flow physics. We will then discuss some early successes and future promise in using similar ideas to study biological data arising in fields like genomics and neuroscience.