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Physically based machine learning for hierarchical materials
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AI
DOI：10.1016/j.xcrp.2024.101790.png)
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Abstract
En 中文
In multiscale phenomena, complex structure -function relationships emerge across different scales, making predictive modeling challenging. The recent scientific literature is exploring the possibility of leveraging machine learning, with a predominant focus on neural networks, excelling in data fitting, but often lacking insight into essential physical information. We propose the adoption of a symbolic data modeling technique, the Evolutionary Polynomial Regression,which integrates regression capabilities with the genetic programming paradigm, enabling the derivation of explicit analytical formulas, finally delivering a deeper comprehension of the analyzed physical phenomenon. To demonstrate the key advantages of our multiscale numerical approach, we consider the spider silk case. Based on a recent multiscale experimental dataset, we deduce the dependence of the macroscopic behavior from lowerscale parameters, also offering insights for improving a recent theoretical model by some of the authors. Our approach may represent a proof of concept for modeling in fields governed by multiscale, hierarchical differential equations.
Keywords:
SPIDER DRAGLINE SILK
MECHANICAL-PROPERTIES
SUPERCONTRACTION
VARIABILITY
STRENGTH
BEHAVIOR
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