Physically based machine learning for hierarchical materials

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@Article{FAZIO:2024:xcrp,

• author = "Vincenzo Fazio and Nicola Maria Pugno and Orazio Giustolisi and Giuseppe Puglisi",
• title = "Physically based machine learning for hierarchical materials",
• journal = "Cell Reports Physical Science",
• volume = "5",
• number = "2",
• pages = "101790",
• year = "2024",
• ISSN = "2666-3864",
• DOI = "doi:10.1016/j.xcrp.2024.101790",
• URL = "https://www.sciencedirect.com/science/article/pii/S2666386424000109",
• keywords = "genetic algorithms, genetic programming, multiscale modeling, data modeling, materials science, spider silk, evolutionary polynomial regression approaches",
• abstract = "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 lower-scale 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.",

}

Genetic Programming entries for Vincenzo Fazio Nicola Maria Pugno Orazio Giustolisi Giuseppe Puglisi

Citations