A note on rapid genetic calibration of artificial neural networks

DOI10.1007/s00466-022-02216-4zbMath1503.74119OpenAlexW4290790622MaRDI QIDQ2086037

Tarek I. Zohdi

Publication date: 20 October 2022

Published in: Computational Mechanics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00466-022-02216-4

zbMATH Keywords

genetic algorithmgenetic-based machine learning algorithmparticle-enhanced composite materialsynapse weight calibration

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Neural networks for/in biological studies, artificial life and related topics (92B20) Composite and mixture properties (74E30) Numerical and other methods in solid mechanics (74S99)

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Cites Work

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• An introduction to computational micromechanics.
• A variational approach to the theory of the elastic behaviour of multiphase materials
• Special issue. Genetic and evolutionary computation
• A digital twin framework for machine learning optimization of aerial fire fighting and pilot safety
• A digital-twin and machine-learning framework for precise heat and energy management of data-centers
• A machine-learning framework for rapid adaptive digital-twin based fire-propagation simulation in complex environments
• Dynamic thermomechanical modeling and simulation of the design of rapid free-form 3D printing processes with evolutionary machine learning
• Electrodynamic machine-learning-enhanced fault-tolerance of robotic free-form printing of complex mixtures
• A digital-twin and machine-learning framework for the design of multiobjective agrophotovoltaic solar farms
• Micromechanical Analysis and Multi-Scale Modeling Using the Voronoi Cell Finite Element Method
• Analysis of Composite Materials—A Survey
• A Variational Approach to the Theory of the Effective Magnetic Permeability of Multiphase Materials
• Random heterogeneous materials. Microstructure and macroscopic properties

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