Join Zoom Meeting
https://cwi-nl.zoom.us/j/81854110402?pwd=YTBvNU9qWHlBaVA2aURISGtKeitSUT09
Meeting ID: 818 5411 0402
Passcode: 599921
Christian Franzke (IBS Center for Climate Physics, Pusan National University in South Korea): Causality Detection and Multi-Scale Decomposition of the Climate System using Machine Learning
Detecting causal relationships and physically meaningful patterns from the complex climate system is an important but challenging problem. In my presentation I will show recent progress for both problems using Machine Learning approaches. First, I will show that Reservoir Computing is able to systematically identify causal relationships between variables. I will show evidence that Reservoir Computing is able to systematically identify the causal direction, coupling delay, and causal chain relations from time series. Reservoir Computing Causality has three advantages: (i) robustness to noisy time series; (ii) computational efficiency; and (iii) seamless causal inference from high-dimensional data. Second, I will demonstrate that Multi-Resolution Dynamic Mode Decomposition can systematically identify physically meaningful patterns in high-dimensional climate data. In particular, Multi-resolution Dynamic Mode Decomposition is able to extract the changing annual cycle.
