Data Science and Beyond: Data Assimilation with Elements of Machine Learning
Organized by Prof. dr. N.A. (Alberto) Carrassihttps://utrechtsummerschool.nl/courses/science/data_science_and_beyond_data_assimilation_with_elements_of_machine_learning
08/24/20 - 08/28/20
Utrecht University, Utrecht, Netherlands
How do meteorologists forecast the weather and climate? Is there a way to predict the profit from a wind farm? These are some of the questions modern science addresses by using data assimilation. Many research institutes and companies (e.g. KNMI, Shell, US-NCAR or UK MetOffice) develop and employ data assimilation and the demand for trained personnel is constantly growing. The school will describe the theoretical foundation of data assimilation together with numerical tutorials, all the way to state-of-the-art methods, including modern machine learning approaches and their combination with data assimilation.
Data assimilation is the science of combining measurement data and computational models. It encompasses a large portfolio of methods at the crossroad between numerical analysis, linear algebra, statistics, dynamical systems and optimal control. Data assimilation is crucial in all circumstances where one wishes to make sense of a model against data and is therefore ubiquitous in science and in real life applications.
The summer school aims at covering the mathematical foundations of data assimilation and at describing the existing methods up to the advanced approaches currently being developed. In particular, the school will address variational and ensemble methods, nonlinear Bayesian techniques for high-dimensional systems and the modern hybrid approaches emerging from the cross-fertilization of data assimilation and machine learning.