Topic Data


In Computational Science data is usually defined as the digital representation of information. The structure of the data range from simple strings or numerical series to high-dimensional, unstructured, or complex concatenated data sets. In simulation, data primarily are the result of a computation. Those data must be analyzed and post-processed to convey their information to the user. However, data, i.e., measured data in general, can determine the initial state in a simulation, for instance in inverse problems in which a model is reconstructed from measured data. Generally, the amount of data within the scope of discretely described problems increases the accuracy of the results. On the other hand, the increase of the amount of the data requires new approaches regarding postprocessing to finally get meaningful physical findings. Hence, there is an urgent need for new high-efficient approaches to extract the a-priori unknown structures and finally the relevant information from the vast amount of data and to prepare them for the user.

Topic Data in the Profile Area

The research topics in the field of data, which is included in the structural areas of pre-processing, simulation and post-processing, are in connection with the above-mentioned examples among others:

  • Data Compression/Compressed Sensing
  • Data extraction/ static and dynamic filtering
  • Real-time transient feature tracking
  • Real-time capable visualization of characteristic features
  • Data-driven modelling

These abstract topics are applied at the RWTH in the fields of energy, mobility, environment, medicine and economy. The work on these research topics within the Data topic area is not focused on individual but rather on several research fields, which again underlines the integrative interdisciplinary research orientation of the RWTH.