Unraveling hidden rules behind the wet-to-dry transition of bubble array by glass-box physics rule learner.

ABSTRACT: A liquid-gas foam, here called bubble array, is a ubiquitous phenomenon widely observed in daily lives, food, pharmaceutical and cosmetic products, and even bio- and nano-technologies. This intriguing phenomenon has been often studied in a well-controlled environment in laboratories, computations, or analytical models. Still, real-world bubble undergoes complex nonlinear transitions from wet to dry conditions, which are hard to describe by unified rules as a whole. Here, we show that a few early-phase snapshots of bubble array can be learned by a glass-box physics rule learner (GPRL) leading to prediction rules of future bubble array. Unlike the black-box machine learning approach, the glass-box approach seeks to unravel expressive rules of the phenomenon that can evolve. Without known pri

SUBMITTER: Cho IH 

PROVIDER: S-EPMC8873482 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature