Investigating the influence of particle distribution on force and torque statistics using hierarchical machine learning

This article attempts to address this bottleneck by availing relatively inexpensive deep learning models. The surrogate models that we employ in this article use a physics-based hierarchical framework and symmetry-preserving neural networks to achieve robustness with limited training data. This article first performs additional generalizability tests on PR data of distinct distributions that are not involved in the training process. The models are then deployed on several different particle distributions. Impact of clustering and structure on the observed statistics are investigated.

Source: AIChE Journal - January 7, 2024 Category: Science Authors: B. Siddani, S. Balachandar, Jiazhong Zhou, Shankar Subramaniam Tags: RESEARCH ARTICLE Source Type: research