Machine Learning for Physics

Contribution List

1. Begrüßung

Bernd Bruegmann, Joachim Giesen

21/05/2019, 13:00

2. From correlation to causality - machine-learning in physics and astronomy (keynote)

Karl Mannheim

21/05/2019, 13:10

Machine-learning methods such as deep learning are frequently used in data-intensive particle physics and astronomy to extract scientific information from data. A number of illustrative expamples will be shown, including the classification of event topologies in particle physics experiments, the morphological and spectral classification of astronomical objects, time-series analysis, or the...

3. GENO: GENeric Optimization for machine learning with an example from physics

Soeren Laue

21/05/2019, 13:50

Optimization is the working horse of machine learning. Our framework (GENO) allows specifying optimization problems in an easy to read modeling language. From such a description a solver is generated automatically, that can solve the specified class of optimization problems. The generated solvers are highly efficient and often outperform hand-written, specialized solvers for machine learning....

4. Perspectives for causal inference in earth system sciences

Jakob Runge

21/05/2019, 14:30

The heart of the scientific enterprise is a rational effort to understand the causes behind the phenomena we observe. In disciplines dealing with complex dynamical systems, such as the Earth system, replicated real experiments are rarely feasible. However, a rapidly increasing amount of observational and simulated data opens up the use of novel data-driven causal inference methods beyond the...

5. Detection and attribution of extreme events in earth observation time series

Maha Shadaydeh

21/05/2019, 14:50

The ability to detect and attribute anomalous behavior in multivariate environmental time series is crucial. These events are signals of changes in the underlying dynamical system and understanding them can help to pave the way for the development of intervention strategy. The availability of high temporal resolution data along with the powerful computing platforms further enhance the capacity...

6. Deep learning for gravitational wave data analysis

Bernd Bruegmann

21/05/2019, 15:10

Gravitational wave data analysis solves a specific problem in signal analysis for time-series data with noise. Recently (starting 2017), deep learning methods have been applied to different aspects of the problem, in particular noise classification, signal detection, denoising, and parameter estimation. We discuss briefly how certain convolutional and recurrent networks work in this context....

7. Sparse + low rank models

Frank Nussbaum, Joachim Giesen

21/05/2019, 15:30

In many applications one is interested in a decomposition of a given matrix into a sparse and a low-rank component. The talk takes a closer look on latent variable graphical models. For these the interaction parameter matrix of a multivariate joint probability distribution is decomposed, where the sparse component corresponds to direct interactions, and the low-rank component depicts spurious...

8. The inverse modelling toolbox

Rainer Heintzmann

21/05/2019, 16:10

Many measurements require solving an inverse problem which is usually large (e.g. in imaging/deconvolution) and often ill-posed. Solving such problems usually requires running an iterative gradient-based optimization. Researchers in those fields need a simple way to specify their inverse modelling problem which is then successively solved. To this aim an inverse modelling (IM) toolbox was...

9. Understanding the earth system with machine learning

Joachim Denzler, Markus Reichstein

21/05/2019, 16:30

The Earth is a complex dynamic networked system. Machine learning, i.e. derivation of computational models from data, has already made important contributions to predict and understand components of the Earth system, specifically in climate, remote sensing and environmental sciences. For instance, classifications of land cover types, prediction of land-atmosphere and ocean-atmosphere exchange,...

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