Machine learning and predicting the time-dependent dynamics of local yielding in dry foams

Abstract

The yielding of dry foams is enabled by small elementary yield events on the bubble scale, “T1”s. We study the large-scale detection of these in an expanding two-dimensional (2D) flow geometry using artificial intelligence (AI) and nearest neighbor analysis. A good level of accuracy is reached by the AI approach using only a single frame, with the maximum score for vertex centered images highlighting the important role the vertices play in the local yielding of foams. We study the predictability of T1s ahead of time and show that this is possible on a timescale related to the waiting time statistics of T1s in local neighborhoods. The local T1 event predictability development is asymmetric in time, and measures the variation of the local property to yielding and similarly the existence of a relaxation timescale after local yielding.