--- English version see below ---
Dies ist eine Einladung zum heutigen Informatischen Kolloquium um 17:15 Uhr.
Die Veranstaltung findet bis auf weiteres online statt.
Der Vortrag mit dem Titel „Guiding Scientific Simulators with Machine Learning" wird von Dr. David Greenberg (Helmholtz Zentrum Hereon Geesthacht) gehalten.
Das Kolloquium wird online mit Zoom abgehalten. Die Zugangsdaten finden Sie unten.
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This is an invitation to today's informatics colloquium (online) at 17:15.
The talk is entitled „Guiding Scientific Simulators with Machine Learning" and will be given by Dr. David Greenberg from Helmholtz Centre Hereon Geesthacht.
The colloquium will be held online using zoom. You will find the access data below.
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Join Zoom Meeting
https://uni-hamburg.zoom.us/j/93735174299?pwd=Ums2cU9EZFpuc0ZFZWFOeWY5eU55dz09
Meeting ID: 937 3517 4299
Passcode: 87303512
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Im Namen des Kolloquiumkomitees
On behalf of the colloquium committee
Carolin Psyk
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Dr. David Greenberg
Guiding Scientific Simulators with Machine Learning
Abstract
Simulations are a powerful tool for combining and exploring scientific insights, and their predictions generalize better to new scenarios than non-physical data-driven aproaches. However, the problem of assimilating noisy and incomplete observations of the simulated system to constrain parameters or initial conditions is challenging, since for many important simulators the data likelihood is intractable. I will describe two recent machine learning-based approaches addresing this problem: Bayesian inference with normalizing flows and optimization with differentiable emulators. Both of these approaches use model simulations as training data, allowing the machine learning model to benefit from the scientfic insight used to design the simulator.
Bio
After a studying mathematics at Brown University, David Greenberg completed his PhD in computational neuroscience and computer vision at the Caesar research institute, Bonn. After completing a Postdoc on simulation-based inference at TUM, he joined Helmholtz Zentrum Hereon in Geesthacht in 2020 as a young investigator group leader, developing machine learning methods for Earth science. His work combines data-centric and physics-based approaches to forecasting, model tuning and uncertainty quantification.
Contact Prof. Dr. Walid Maalej