Researchers from manufacturing or pharmaceutical industry
Data-driven models have gone everywhere: Backed by their successes in comparably recent technological fields such as image processing or speech recognition and powered by ongoing digitization, data-driven models have been taken up in all disciplines. In some of these disciplines, a large amount of domain knowledge is available in the form of (physical) theories – for example, in manufacturing or pharmaceutical industries, this domain knowledge may come in the shape of differential equations representing the behavior of materials or the reactions within a process describing the system under study.
Rather than suggesting data-driven models as a substitute for this theoretical knowledge, the present talk will give an insight into how this knowledge can be incorporated into the data-driven models. The combined – hybrid – model will generally have a larger consistency with the physical world and, due to the use of data, a higher accuracy than its theoretical counterpart. We will illustrate the various ways to combine theoretical with data-driven models at the hand of several success stories achieved at the Know-Center.
In the second part of the session, we ask for your input: What is the type of knowledge available in your profession? And for what problems do you currently foresee data-driven models as promising approaches? What are the major roadblocks that you see ahead? Allocating speaker time to all attendees will open the forum for discussing how theory-guided data science can be put to good use in your respective field.
After the event you will know:
The manifold possibilities of incorporating theoretical knowledge into data-driven models, e.g.,
- How deep learning can solve differential equations
- How domain knowledge can help in feature engineering
- How a data-driven approach can improve approximate physical models
Furthermore, you will learn about
- Previous success stories in theory-driven data science and hybrid modelling
- The future research direction of Know-Center in this field