Project Details
Projekt
Translating thermodynamic knowledge to computers
Applicants
Professorin Dr. Sophie Fellenz; Professor Dr.-Ing. Hans Hasse; Professorin Dr. Heike Leitte
Subject Area
Chemical and Thermal Process Engineering
Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing
Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing
Term
since 2021
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 466468799
This project addresses one of the fundamental topics of artificial intelligence: the translation of human knowledge to computers. Our ambition is to achieve this for the vast and deep knowledge on thermodynamics. Thermodynamics is particularly suited for this endeavor as it is a field with a highly developed and well-structured theory; and a successful translation would be particularly rewarding, as thermodynamics belongs to both science and engineering, and has extremely wide applications. It can be assumed that the basic principles that guide the translation of the thermodynamic knowledge can also be applied to other fields of science and engineering. Thermodynamic theory is rightfully considered to be one of the masterpieces of the human mind and making this theory accessible to machines has so far been hardly conceivable. In preliminary work, we have established a promising route to tackle this challenge. The knowledge is thereby condensed in two interacting graphs; which, together, can be used for creating thermodynamic models of real world objects, analyze them and answer question regarding their thermodynamic behavior. The first graph structures the ther-modynamic knowledge, the second provides the corresponding equations. The system will have a linguistic input/output based on deep learning techniques that are combined here with the thermodynamic knowledge. It aims not only at solving problems; it is also useful for storing and retrieving thermodynamic knowledge, and, last not least, it may change paradigms in teaching thermodynamics.
DFG Programme
Priority Programmes
