Multiphysics-informed deep learning for swelling of pH/temperature sensitive cationic hydrogels and its inverse problem

This paper proposes a field theory of constrained swelling of pH/temperature sensitive cationic hydrogels in equilibrium with their chemical and mechanical environment. A general formulation is obtained based on a variational approach, yielding a set of governing equations coupling mechanical and chemical equilibrium conditions, which is employed to investigate some benchmark problems involving homogeneous and inhomo-geneous swelling of the pH/temperature sensitive cationic hydrogels. The simulation results are compared with experimental data available in the literature to verify the present model. By encoding the underlying physical and chemical laws into the deep learning neural networks as prior information, we introduce the multiphysics-informed deep learning (MIDL) to investigate the effects of temperature and pH on the distributions of con-centration of solvent and stresses in the hydrogel shell. In addition, the MIDL is extended to solve inverse identification problem of inhomogeneous swelling of core-shell hydrogels, which yields a reasonable identifi-cation accuracy even if the observed data is corrupted due to uncorrelated noise.

Automated summary

This paper proposes a field theory for the constrained swelling of pH/temperature sensitive cationic hydrogels. A variational approach is used to obtain a set of governing equations that couple mechanical and chemical equilibrium conditions. The proposed model is validated through comparison with experimental data, and the effects of temperature and pH on solvent concentration and stress distribution in the hydrogel shell are investigated using multiphysics-informed deep learning. The deep learning model is also extended to solve the inverse identification problem of inhomogeneous swelling of core-shell hydrogels.