Some applications of machine learning in active matter

Add to your list(s) Download to your calendar using vCal

• Eric Vanden-Eijnden, Courant Institute of Mathematical Sciences, New York University
• Tuesday 26 November 2024, 13:00-14:00
• Center for Mathematical Sciences, Lecture room MR4.

If you have a question about this talk, please contact Balázs Németh.

Machine learning (ML) techniques are changing Science and Engineering by offering ways to reconsider complex problems once thought intractable because of the curse of dimensionality. In this talk, I will discuss the impact of ML on applications from active matter. Specifically I will show how physics-informed neural networks (PINNs) can be used to analyze first-order phase transitions in non-equilibrium systems, and how advances in generative modeling can be leveraged to characterize the breakup of time-reversal symmetry (TRS) and calculate entropy production rates (EPRs) in active systems. I will also discuss how the standard modus operandi of ML must be adapted in the context of such applications when they come with models and no data (as opposed to data and no models), and thereby require to use active learning strategies for data acquisition.

This talk is part of the DAMTP Statistical Physics and Soft Matter Seminar series.

This talk is included in these lists:

• All CMS events
• Center for Mathematical Sciences, Lecture room MR4
• DAMTP Statistical Physics and Soft Matter Seminar
• Soft Matter
• bld31

Note that ex-directory lists are not shown.