10 May 2021
Colloquium by Dr. David Greenberg, Helmholtz Centre Hereon Geesthacht
Monday, 10 May 2021 17:15, Zoom Meeting
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.
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