Collaborative Research Center 1456
Mathematics of Experiment
The challenge of indirect measurements in the natural sciences
Spring School 2024
Motivated by applications studied in CRC 1456, ranging from helioseismology to electron microscopy, this CRC 1456 Spring School will focus on modeling of new types of wave data, corresponding forward problems, and novel inversion techniques. The spring school will take place at the Convention Centre by the - Historical Observatory Geismarer Landstraße 11b - 37083 Göttingen on March 11 to 14, 2024.
You will find more information here.
Waves 2024
The 16th International Conference on Mathematical and Numerical Aspects of Wave Propagation (June 30 to July 5) is the latest in the series of very successful WAVES conferences. The conference will cover a wide range of mathematical and numerical techniques applied across disciplines. It will bring together mathematicians and physicists of all backgrounds - established researchers, postdocs and students.
The conference venue is the Harnack House in Berlin-Dahlem.
You will find more information here.
Podcast series (only in German)
1. Funding period (2021 - 2024)
We witness an era where unprecedented amounts of data are acquired in experimental research in the natural sciences. While new measurement techniques and instruments keep being devised and improved for inexpensive and efficient data acquisition, the current bottleneck is how to extract meaningful information from the resulting vast amounts of such measurements. Typical reasons are that modern measurement technologies often provide such information only in an indirect manner and that the observational data are strongly corrupted by noise and often generated in an inherently random way. The goal of this Collaborative Research Center is to contribute to the efficient extraction of maximal quantitative information from experimental data, backed by mathematical modelling and analysis.
Research in this CRC is steered by data. We focus on three types of structures that are abundantly prevalent in experimental data:
- data with geometric nonlinearities,
- data with incomplete information, and
- data with information in their dependency structure.
Progress made in data science in recent years will be incorporated and combined with model-based approaches to develop techniques for analysing scientific data. Applications range from condensed matter physics, molecular or cellular biophysics, biomedical research to astronomy.