Dear All,

The next speaker in our CWI seminar for Machine Learning and Uncertainty Quantification for Scientific Computing will be Christian Franzke from IBS Center for Climate Physics at the Pusan National University in South Korea. The talk will be on Wednesday the 23rd, at 10AM CEST. The topic will be the detection of causal relationships and physically meaningful patterns from the complex climate system, using reservoir computing and multi-resolution dynamic mode decomposition. The zoom link and abstract can be found below. As usual, feel free to share the zoom link with your colleagues.

Kind regards,

Wouter Edeling

23 Jun. 2021 10h00 CET: 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.

Join Zoom Meeting https://cwi-nl.zoom.us/j/81854110402?pwd=YTBvNU9qWHlBaVA2aURISGtKeitSUT09

Meeting ID: 818 5411 0402 Passcode: 599921