SMS scnews item created by Lachlan Smith at Tue 8 Sep 2020 0840
Type: Seminar
Distribution: World
Expiry: 3 Nov 2020
Calendar1: 11 Sep 2020 1600-1700
CalLoc1: Zoom
Auth: lachlans@105.66.233.220.static.exetel.com.au (lsmi9789) in SMS-WASM

# Sydney Dynamics Group: Georg Gottwald -- Supervised learning from noisy observations: Combining machine-learning techniques with data assimilation

Dear all,

This week, Friday September 11, Georg Gottwald (USyd) will give a talk at 4pm (Sydney
time) via Zoom.

Meeting ID: 941 6360 0398

Title: Supervised learning from noisy observations: Combining machine-learning
techniques with data assimilation

Abstract:
Data-driven prediction and physics-agnostic machine-learning methods have attracted
increased interest in recent years achieving forecast horizons going well beyond those
to be expected for chaotic dynamical systems.  In a separate strand of research
data-assimilation has been successfully used to optimally combine forecast models and
their inherent uncertainty with incoming noisy observations.  The key idea in our work
here is to achieve increased forecast capabilities by judiciously combining
machine-learning algorithms and data assimilation.  We combine the physics-agnostic
data-driven approach of random feature maps as a forecast model within an ensemble
Kalman filter data assimilation procedure.  The machine-learning model is learned
sequentially by incorporating incoming noisy observations.  We show that the obtained
forecast model has remarkably good forecast skill while being computationally cheap once
trained.  Going beyond the task of forecasting, we show that our method can be used to
generate reliable ensembles for probabilistic forecasting as well as to learn effective
model closure in multi-scale systems.
This is joint work with Sebastian Reich.

Past talks can be found on the YouTube channel: