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KITP Program: Machine Learning and the Physics of Climate
(Nov 1 - Dec 17, 2021)
Coordinators: Annalisa Bracco, Henk A. Dijkstra, Claire Monteleoni, and Laure Zanna

Speakers: Please contact us about file upload for your slides.

Time Speaker Title
12/15, 2:00pm Frank Kwasniok
Exeter
Data-driven deterministic and stochastic subgrid-scale parametrization in atmosphere and ocean models: a pattern-based approach[Embargoed]
12/15, 9:30am Yan Liu
University of Southern California
Differential Graph Neural Networks for Physics-Informed AI Models[Video]
12/13, 2:00pm Henk Dijkstra
Utrecht Univ.
Machine Learning and the Physics of Climate[Video]
KITP Blackboard Lunch
12/13, 11:30am Christian Lessig
Univ. Magdeburg
A Deep Neural Network Multigrid Solver[Slides][Video]
12/13, 9:30am Annalisa Bracco
Georgia Tech
Informal Talk: Lion fish, corals, connectivity, SSTs and d-MAPS[Video]
12/08, 9:30am Alex Robel
Georgia Tech
Statistical learning of climate for large ensemble ice sheet simulations[Slides][Video]
12/06, 11:30am Erik Mulder
Univ. of Gronigen
Symbiotic ocean modeling using physics-controlled Echo State Networks[Embargoed]
12/06, 9:30am Julien Brajard
NERSC
Bridging observations and numerical modeling using machine learning[Video]
12/01, 2:00pm Martin Schultz
Julich Centre
Deep Learning for Air Quality and Weather Forecasting[Slides][Video]
12/01, 9:30am Markus Abel
Ambrosys GmbH
Symbolic regression and mathematical postprocessing for machine learning of (climate) dynamics[Video]
11/29, 9:30am Freddy Bouchet
ENS Lyon
Predicting extreme heat waves using rare event simulations and deep neural networks[Video]
11/24, 9:00am Raffaele Ferrari
MIT
New approaches to calibration of parameterizations of boundary layer turbulence[Video]
11/22, 9:30am Bia Villas Boas
Colorado School of Mines/Caltech
From noise to signal: what surface waves can teach us about currents[Video]
11/17, 9:30am Andreas Gerhardus
DLR
Learning cause-and-effect relationships from time series data[Video]
11/15, 9:30am Brian White
UNC
Deep learning applications for climate and weather modeling: toward improvements in speed, resolution and scenario generation[Embargoed]
11/10, 9:00am Deborah Khider
USC
The challenges of using paleoclimate data for decadal prediction[Slides][Video][CC]
11/08, 9:00am Dmitri Kondrashov
UCLA
Data-driven stochastic climate modeling and prediction[Slides]
11/1-4 Conference: Machine Learning for Climate