Predicting Plasticity in Two-Dimensional Foam Channel Flow Around an Obstacle

Abstract

We study the prediction of plastic activity in the confined channel flow of two-dimensional amor- phous soft particles around a circular obstacle. Using datasets generated with a particle-based bubble model, we formulate the prediction problem within two supervised-learning frameworks: re- gression of the non-affine displacement and binary classification of neighbor change events. A key technical challenge is that the obstacle and the confining walls explicitly break translational and rotational symmetries. We address this issue by introducing additional structural descriptors that encode the positions of particles relative to these boundaries. Starting from simple linear models, we systematically increase the complexity of the learning framework by considering a logarithmic transformation of the target variable, the incorporation of particle-size information, the addition of symmetry-breaking obstacle and wall descriptors, the coarse-graining of local structural descrip- tors, and nonlinear neural-network models. We find that the obstacle and wall descriptors provide the largest improvement in predictive performance. Nevertheless, the models considered here cap- ture mainly the overall localization of plastic activity near the obstacle and do not fully reproduce its detailed heterogeneous pattern in individual configurations. A perturbation analysis indicates that this heterogeneity is robustly encoded in the initial structure, suggesting that further progress requires more expressive structural representations and machine-learning architectures.