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A framework for self‐evolving computational material models inspired by deep learning
Author(s) -
Cho In Ho
Publication year - 2019
Publication title -
international journal for numerical methods in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.421
H-Index - 168
eISSN - 1097-0207
pISSN - 0029-5981
DOI - 10.1002/nme.6177
Subject(s) - computer science , artificial intelligence , deep learning , similarity (geometry) , index (typography) , process (computing) , scheme (mathematics) , theoretical computer science , machine learning , mathematics , mathematical analysis , world wide web , image (mathematics) , operating system
Summary There exists a deep chasm between machine learning (ML) and high‐fidelity computational material models in science and engineering. Due to the complex interaction of internal physics, ML methods hardly conquer or innovate them. To fill the chasm, this paper finds an answer from the central notions of deep learning (DL) and proposes information index and link functions, which are essential to infuse principles of physics into ML. Like the convolution process of DL, the proposed information index integrates adjacent information and quantifies the physical similarity between laboratory and reality, enabling ML to see through a complex target system with the perspective of scientists. Like the hidden layers' weights of DL, the proposed link functions unravel the hidden relations between information index and physics rules. Like the error backpropagation of DL, the proposed framework adopts fitness‐based spawning scheme of evolutionary algorithm. The proposed framework demonstrates that a fusion of information index, link functions, evolutionary algorithm, and Bayesian update scheme can engender self‐evolving computational material models and that the fusion will help rename ML as a partner of researchers in the broad science and engineering.