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

In Ho Cho, Sinchul Yeom, Tanmoy Sarkar, Tae Sik Oh

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

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 principles, GPRL identifies plausible rules of bubble prediction with an elongated bubble array data that transitions from wet to dry states. Then, the best-so-far GPRL-identified rule is applied to an independent circular bubble array, demonstrating the potential generality of the rule. We explain how GPRL uses the spatio-temporal convolved information of early bubbles to mimic the scientist’s perception of bubble sides, shapes, and inter-bubble influences. This research will help combine foam physics and machine learning to better understand and control bubbles.

Original languageEnglish
Article number3191
JournalScientific Reports
Volume12
Issue number1
DOIs
StatePublished - Dec 2022
Externally publishedYes

Funding

This work was supported (in part, I.C.) by the National Science Foundation (NSF) under grant OAC-1931380 and CMMI-2129796. The computational simulation reported in this paper is partially supported by the high-performance computing equipment at Iowa State University, some of which have been purchased through funding provided by NSF under MRI grant number CNS 1229081and CRI grant number 1205413. This work was (in part, T.O.) financially supported by Auburn University.

FundersFunder number
National Science FoundationCMMI-2129796, OAC-1931380
Auburn University
Materials Research Institute, Pennsylvania State University1205413

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