Designing Learning Machines for industrial Applications: from Transfer Learning to Physics Informed Machine Learning Models.

In recent years, significant progress has been made in setting up decision support systems based on machine learning exploiting very large databases.   In many research or production environments, the available databases are not very large, and the question arises as to whether it makes sense to rely on machine learning models in this context.
Especially in the industrial sector, designing accurate machine learning models with an economy of data is nowadays a major challenge.
This talk presents Transfer Learning and Physics Informed Machine Learning models that leverage various knowledge to implement efficient models with an economy of data.
Several achievements are presented that successfully use these learning approaches to develop powerful decision support tools for industrial applications, even in cases where the initial volume of data is limited.