Physics-Informed Machine Learning – Lecture 1 | Why Physics + AI?

to you everyone to be here and it's a pleasure because it's it's a long time that I'm trying to plan this course for PhD and eventually now it's here. So with Mattia that give that prepare most of the the slides that you will see. Um we will talking about physics-informed machine learning in

more general way so then we will focus in particular not only to physics-informed because there is a little mismatch in the taxonomy but this is more uh wide range of possibility to embed in physics. So for planning, modeling, and control and estimation physical system so for any kind of physical system you want commonly we will focus on

robotics on body but I mean the the the techniques that we will present in this course is not focus on only on rigid body. Uh so I am Gastone Pietro Rosati Papini and I am a professor here in the department of industrial engineering. So now we will start. I will ask to Mattia to to start the presentation.

Thank you Mattia. Please. >> Yeah, of course. Yeah, thanks Gastone also for the introduction. So yes, welcome everybody to the course also the ones following online. Um so indeed yeah, I'm Mattia Piccinini. I'm a currently a post-doctoral researcher at the Technical University of Munich in Germany and I'm working most of the time on on these topics so

physics-informed machine learning for different types of applications especially in robotics but also sometimes more in general in physical systems. Um so yeah, we would like to keep the course uh um, of course interactive. So, also be free to ask questions. Maybe when we are if we are a bit more people so from online we can also try to