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Inverting the Beamline

Steinbach, P.

In many domains of modern physics, we encounter the situation that generations of scientists have created high precision simulations of the effects under study. Today, these simulations have become essential to the scientific method. However, these
(often mechanistic) simulations of high predictive power carry with them a burden of inference. Once a forward process has been simulated, an inversion of a simulation given observed data from experiments is challenging, sometimes even impossible.

In this presentation, I'd like to provide an introduction to simulation based inference for inverting a beamline simulation at BESSY in Berlin. In this project, I studied the inversion of a beamline simulation using state-of-the-art machine learning. We will start our journey with normalizing flows, walk by conditional invertible neural networks and finish with Automatic Posterior Transformation for Likelihood-Free Inference. To stay with the metaphor: please bring your mathematical boots, wear a hat of Bayes Law and bring your best compass of statistics - otherwise you likely get lost in about a quarter of the presentation.

Keywords: beamline; simulation; normalizing flows; posterior; inverse problem; machine learning

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Permalink: https://www.hzdr.de/publications/Publ-32750
Publ.-Id: 32750