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March 17, 2022
Title: Large Scale Data Assimilation
Authors: Sebastian Friedemann and Bruno Raffin
Abstract:
How to combine data, which may be available though different sensors,
with traditional numerical solvers designed to compute solutions of
PDEs modeling complexe phenomenon ? Bridging both is a timely
question with the multiplication of data sources (IoT). But
augmenting solvers working in high dimension spaces with external
data is far from trivial. Both are usually subject to uncertainties.
Data Assimilation (DA) is a well known approach to address this issue. DA is actually
routinely used in production for weather forecast for instance. In this talk, I will first motivate and
introduce the principles of Data Assimilation, including the
different families of techniques (statistical, variational) with a
specific focus on statistical ones (EnKF and particle filter).
From there, I will explain the worked performed with the
Melissa framework to support very large scale statistical data
assimilation with the EnKF method. We will dive into the details
of the software infrastructure that has been designed to go to very large scale,
supporting features like elasticity, fault tolerance and dynamics
load balancing, and show some experimental performance results.
