Workshop on Machine Learning for Physics 2019

Machine learning is currently taking various scientific disciplines by storm and physics is no exception. This workshop concentrated on applications of machine learning in physics. In his keynote, Karl Mannheim of University of Würzburg sketched this fascinating and growing field. He argued that there is an urgent need to distinguish between correlations and causality. Another focus of this workshop, which was addressed by three talks, was on applications of machine learning in Earth System Sciences.

Here is a collection of the titles of the presentations:

Title of Presentation Speaker Affiliation
From Correlation to Causality – Machine Learning in Physics and Astronomy Prof. Dr. Karl Mannheim University of Würzburg
GENO: GENeric Optimization for Machine Learning with an Example from Physics PD Dr. Sören Laue FSU Jena
Perspectives for Causal Inference in Earth System Sciences Dr. Jakob Runge German Aerospace Center
Detection and Attribution of Extreme Events in Earth Observation Time Series Dr. Maha Shadaydeh FSU Jena
Deep Learning for Gravitational Wave Data Analysis Prof. Dr. Bernd Brügmann FSU Jena
Sparse + Low Rank Models Frank Nussbaum and Prof. Dr. Joachim Giesen FSU Jena
The Inverse Modelling Toolbox Prof. Dr. Rainer Heintzmann FSU Jena
Understanding the Earth System with Machine Learning Prof. Dr. Joachim Denzler and Prof. Dr. Markus Reichstein FSU Jena and MPI Biogeochemistry