Machine Learning based Data Analysis

We develop data analysis concepts based on machine learning. In this context, we pursue several scientific and software methodological goals:

1. Development of a universally valid experimental data
2. Generalisation of post-processing steps such as binarization, spatial and temporal filtering of data,
3. Generation of synthetic training data from experimental data using novel methods like domain randomisation.
   1. 1. Development of our own data generation framework that can assemble a complete dataset (~10000 images) from a database of interested objects with proper annotation standards.
      2. Enhancement of the framework by parallelisation and addition of different types of data generation capabilities.
4. Coupling of conventional methods with AI
   1. 1. We incorporate AI into our existing data analysis framework:
         • Image segmentation into signal and background via Instance Segmentation. Here we use, among others, state-of-the-art Deep Neural Networks such as Mask-RCNN and SparseInst.
         • Optical flow detection on PIV data. We try to adapt general use SOTA models (like RAFT) to our PIV measurements to accurately infer velocity fields and displacements.
5. Automation of the AI model updating and hyperparameter tuning
   1. 1. Incorporation of reinforcement learning to automatically correct false detections and self-optimise over time.
      2. Enhancement of training process by automatically preselecting the most relevant parts of the training data by context.
6. Development of a complete AI assisted data analysis suite
   1. 1. All these separate AI tasks are brought together under one common roof in the form of an industrial standard software suite.

01.01.2022 – 31.12.2027

If you are interested in our project, have further questions, or would like to support us through student work, internships, or thesis projects, we would be delighted to hear from you via email or phone. Contact details can be found below.