4. feb. 2022
On the 18th December a webinar with the title "Data Quality and Hybrid Analytics" was arranged by CPACT. The presentation was given by Frode Brakstad and Sølve Eidnes, SINTEF.
Since it’s breakthrough in the last decade, AI has been most prominent and promising on problems that are digital in nature, like image analysis, language models and targeted advertising. Industry applications can include physical, chemical and biological systems that bring along several complicating issues. Most industrial systems at present are often highly complex and of a nature that makes it difficult to get data of sufficient quality and quantity to use purely data-driven methods. Furthermore, one often must consider safety-critical systems, which puts extra requirement on the precision and trustworthiness of the models used.
The methods best suited to deal with these challenges may lie within the field of hybrid analytics, or physics-informed machine learning. By combining data-driven modelling (assuming high-quality data) with the available prior knowledge about the system, we can get models that learn better and are more robust than purely data-driven models, while generalizing better and being more flexible than classical models.