CMCC Webinar | 25 February 2021 at 15.00 (CET)
Rossella Arcucci, Data Science Institute, Imperial College London
Moderator: Nadia Pinardi, CMCC Foundation
Over the past few years, Data Assimilation (DA) have increased in sophistication to better fit application requirements and circumvent implementation issues. Nevertheless, these approaches are incapable of fully overcoming their unrealistic assumptions. Machine Learning (ML) shows great capability in approximating nonlinear systems, and extracting high–dimensional features. ML algorithms are capable of assisting or replacing traditional forecasting methods. However, the data used during training in any ML algorithm include numerical, approximation and round off errors, which are trained into the forecasting model. Integration of ML with DA increases the reliability of prediction by including information with a physical meaning. This talk provides an introduction to Data Learning, a field that integrates Data Assimilation and Machine Learning to overcome limitations in applying these fields to real-world data. The fundamental equations of DA and ML are presented and developed to show how they can be combined into Data Learning. We present a number of Data Learning methods and results for some test cases, though the equations are general and can easily be applied elsewhere.
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Fondazione CMCC – Centro Euro‐Mediterraneo sui Cambiamenti Climatici