The Machine Learning Embedded Method of Parameters Determination in the Constitutive Models and Potential Applications for Hydrogels
Shoujing Zheng, Zishun Liu
Abstract
We propose a machine learning embedded method of parameters determination in the constitutional models of hydrogels. It is found that the developed logistic regression-like algorithm for hydrogel swelling allows us to determine the fitting parameters based on known swelling ratio and chemical potential. We also put forward the neural networks-like algorithm, which, by its own property, can converge faster as the layer deepens. We then develop neural networks-like algorithm for hydrogel under uniaxial load for experimental application purpose. Finally, we propose several machine learning embedded potential applications for hydrogels, which would provide directions for machine learning-based hydrogel research.
Topics & Concepts
Self-healing hydrogelsArtificial neural networkComputer scienceSwellingProperty (philosophy)Artificial intelligenceLayer (electronics)Machine learningAlgorithmMaterials scienceBiological systemNanotechnologyComposite materialPolymer chemistryPhilosophyEpistemologyBiologyHydrogels: synthesis, properties, applicationsAdvanced Theoretical and Applied Studies in Material Sciences and GeometryOptical Imaging and Spectroscopy Techniques