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Deep neural networks for large deformation of photo-thermo-pH responsive cationic gels. (English) Zbl 1481.74042

Summary: In this work, a model is developed to analyze homogeneous and inhomogeneous large deformation of photo-thermo-pH responsive cationic gels. Constitutive equations are achieved by considering the equilibrium thermodynamics of swelling gels through variational method. Employing this model, coupling effects of light intensity, temperature and pH variations on large deformation of gels are analyzed. The simulation results are compared with available experimental data. Then deep neural networks are developed to approximate solutions to equilibrium equations of inhomogeneous swelling of spherical shell structure gels. The volume phase transition temperature of the gels and their dependence on light intensity are also demonstrated.

MSC:

74A20 Theory of constitutive functions in solid mechanics
92B20 Neural networks for/in biological studies, artificial life and related topics

Software:

DeepXDE
Full Text: DOI

References:

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