Physikalisches Kolloquium: Prof. Dr. Martin Oettel – Machine Learning approaches to Classical Many-Body Systems in (Non-)Equilibrium

Machine Learning approaches to Classical Many-Body Systems in (Non-)Equilibrium

Abstract: Classical many-body systems comprise everyday liquids, colloidal systems or almost everything in the realm of soft matter.
In equilibrium, all properties can be deduced from the one-body density profile (the space-dependent probability for finding a particle).
This is guaranteed by the therorems of Density Functional Theory (DFT), but one needs the functional of the free energy to put DFT to work.
For many systems, even very simple ones, this functional is not known.
I discuss recent advances and perspectives on finding these functionals using methods of Machine Learning (ML) and try to build a bridge also
to the quantum DFT problem where similar developments are in progress. Also, the general classical nonequilibrium problem can be put in a
functional form (power functional theory), and the likewise unknown functional of dissipated power should be learnable by ML methods.

Sprecher / Speaker: Prof. Dr. Martin Oettel, Uni Tübingen
Kontakt / Contact: Prof. Dr. Klaus Mecke

Norbert Lindlein lädt Sie zu einem geplanten Zoom-Meeting ein.
Norbert Lindlein invites you to a planned Zoom meeting.

Thema: Physikalisches Kolloquium 2024
Zoom-Link:
https://fau.zoom-x.de/j/61610033904?pwd=QTRpUThPZEg5YVBPd3pFYzVHdGYvUT09
Meeting-ID: 616 1003 3904, Kenncode: 285966