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 knowledge within them, can help with those different issues and offer a promising avenue for climate applications and process understanding. BIO Pierre Gentine is an associate professor in Earth and Environmental Engineering at Columbia. He studies the terrestrial water and carbon cycles and their changes with climate change. Pierre Gentine is recipient of the NSF, NASA and DOE early career awards, as well as the American Geophysical Union Global Environmental Changes Early Carrer and American Meteorological Society Meisinger award.
[Virtual] GFDL Lunchtime Seminar
Wed, Jun 24, 2020, 12:00 pm to 1:00 pm