David Sondak: Fluid Mechanics with Turbulence, Reduced Models, and Machine Learning | IACS Seminar скачать в хорошем качестве

David Sondak: Fluid Mechanics with Turbulence, Reduced Models, and Machine Learning | IACS Seminar

Presenter: David Sondak, Lecturer at the Institute for Applied Computational Science, Harvard University Abstract: Fluids are everywhere. As humans, we are constantly surrounded by them, including the air we breathe, the blood in our bodies, the water in the oceans, and the solar wind bombarding the Earth. Indeed, fluids impact every area of science from the biological to the geophysical and astrophysical. Understanding and controlling fluid behavior has an immense impact on human society from more eective drug delivery techniques through more ecient energy harvesting technologies. However, the desire to understand and control fluid behavior gives rise to signicant mathematical challenges in the form of multiscale behavior, complex geometries, and complex fluids. Dr. Sondak will begin his talk with an introduction to fluid mechanics and why it is an important field of study. He will then motivate numerical methods and multiscale phenomena before giving an overview of turbulence. He will discuss reduced order models from the perspective of turbulence, including turbulence modeling. Dr. Sondak will utilize the last portion of his talk to present recent progress on using machine learning to develop and improve turbulence models. Speaker Bio: David Sondak is a lecturer at the Institute for Applied Computational Science where he teaches courses on computational science and data science. David received his PhD in Aeronautical Engineering in 2013 from Rensselaer Polytechnic Institute during which time he was funded through the Department of Energy Office of Science Graduate Fellowship program. He received a Master's degree in Applied Mathematics, also from RPI, in 2011 and a Bachelor's degree in Mechanical Engineering from Lehigh University in 2008. Prior to joining IACS, David was a postdoctoral research fellow in the Institute for Computational Engineering and Sciences (ICES) at the University of Texas at Austin where he conducted research on representing uncertainties in models of chemical kinetics. In that work, he made extensive use of Bayesian statistical techniques to provide meaningful interpretations of uncertainties. Before joining ICES, David was a Van Vleck Visiting Assistant Professor in the Department of Mathematics at the University of Wisconsin-Madison. He taught courses ranging from second semester calculus through linear algebra and partial differential equations. In addition to teaching, he researched the fundamental structures governing heat transport in Rayleigh-Benard convection. David's ongoing research focuses on the development of physics-based, data-driven models to enable predictive capabilities in problems of engineering and scientific interest including magnetohydrodynamics, thermal convection, and chemical kinetics.