Seminar: Hybrid modeling: best of both worlds? / Pierre Gentine
February 6, 2020, 12:00 PM
Pierre Gentine
Associate Professor, Earth & Environmental Engineering
Earth Institute
Data Science Institute
Columbia University
Abstract:
In recent years, we have witnessed an explosion in the applications of machine learning, especially for environmental problems. Yet for broader use, those algorithms may need to respect exactly some physical constraints such as the conservation of mass and energy. In addition, environmental applications (e.g. drought, heat waves) are typically focusing on extremes and on out-of-sample generalization rather than on interpolation. This can be a problem for typical algorithms, which interpolate well but have difficulties extrapolating. I will here show how a hybridization of machine learning algorithms, imposing physical constraints within them, can help tackle those different issues and offer a promising avenue for climate applications and process understanding.
Bio:
Pierre Gentine is an Associate Professor in the department of Earth and Environmental Engineering in the School of Engineering and Applied Sciences. He is an Investigator in the Columbia Water Center and a director of the Graduate Program in Earth and Environmental Engineering. Dr. Gentine received his undergraduate degree from SupAéro, in France. He earned his PhD in Civil and Environmental Engineering at MIT in 2010. He joined the faculty at Columbia in 2010 as an instructor in applied mathematics and then as a tenure track assistant professor in Earth and Environmental Engineering in 2011. Dr. Gentine investigates the continental hydrologic cycle though land-atmosphere interaction, boundary layer turbulence, convection, ecohydrology and remote sensing.
Website:
https://gentinelab.eee.columbia.edu/
Title: Hybrid modeling: best of both worlds?
Speaker: Pierre Gentine
Associate Professor, Earth & Environmental Engineering
Columbia University