This seminar series aims at presenting research related to mechanics in the broad sense. Each talk lasts 45 minutes (including questions) and is normally followed by small refreshments.
Anyone is welcome to join; the seminars are usually held on Fridays at 12:15 pm in Niels Henrik Abels Hus, room 919. You can subscribe to the mailing list to receive alerts for upcoming seminars and contact Stéphane Poulain if you have questions.
The frictional behavior of surfaces is a problem of great scientific and practical significance. Recent progress in molecular scale modeling allows us to determine the coefficient of friction for nanoscale surfaces from first principles using molecular dynamics modeling. However, inverse design, that is, designing surfaces with specific frictional propeties is still a complex and largely unsolved challenge in part due to the enormous space of possible surface configurations. Here, we demonstrate how we can use physical forward modeling to find the frictional properties of a set of surfaces that can serve as a training set to design machine learning models. In this talk, we demonstrate both discriminative and generative models for frictional surface design and analyze what physical principles the machine learning models have learned in this process.