A framework for glass-box physics rule learner and its application to nano-scale phenomena

Autor: Qiang Li, Rana Biswas, Jaeyoun Kim, In Ho Cho
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: Communications Physics, Vol 3, Iss 1, Pp 1-9 (2020)
ISSN: 2399-3650
Popis: Attempts to use machine learning to discover hidden physical rules are in their infancy, and such attempts confront more challenges when experiments involve multifaceted measurements over three-dimensional objects. Here we propose a framework that can infuse scientists’ basic knowledge into a glass-box rule learner to extract hidden physical rules behind complex physics phenomena. A “convolved information index” is proposed to handle physical measurements over three-dimensional nano-scale specimens, and the multi-layered convolutions are “externalized” over multiple depths at the information level, not in the opaque networks. A transparent, flexible link function is proposed as a mathematical expression generator, thereby pursuing “glass-box” prediction. Consistent evolution is realized by integrating a Bayesian update and evolutionary algorithms. The framework is applied to nano-scale contact electrification phenomena, and results show promising performances in unraveling transparent expressions of a hidden physical rule. The proposed approach will catalyze a synergistic machine learning-physics partnership. Using machine learning to interpret complex phenomena and reveal unknown physical rules is an active research frontier. Here, the authors address how to combine basic physics, a convolved information index, and a transparent flexible link function to identify mathematical expressions of the underlying physical processes of nanoscale contact electrification.
Databáze: OpenAIRE
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