Collaborative Research Center 1456
Mathematics of Experiment
The challenge of indirect measurements in the natural sciences
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.