Our team over the years has collaborated with the Institute of Informatics & Telecommunications. One of our missions is to solve open problems in nanometrology through Machine Learning based methods.
This year, along with Dr. George Giannakopoulos for one of our approaches, we used machine learning models to help the designer predict important nano-functionalities (wetting) through their nano-structure parameters.
What is the cost to build such machine learning models to do the predictions?
What is required to do that?
How can you combine domain knowledge and AI?
In our participation in SETN 2020 and through our workshop proceeding we attempt to answer the questions above.
Stellas A., Giannakopoulos G., Constantoudis V., Hybridizing AI and domain knowledge in nanotechnology: The example of surface roughness effects on wetting behavior (2020) CEUR Workshop Proceedings, 2844, pp. 48-54.
