Adapting sigmoid functions for hydrogel swelling curve prediction with neural networks

Research output: Contribution to journalResearch articleContributed

Abstract

Stimuli‐responsive hydrogels are representatives of smart materials with enormous swelling capability. The prediction of discrete hydrogel swelling states by artificial neural network based on the processing parameters has been realized in our previous work. In the current study, we explore ways to enhance the prediction capabilities by integrating physical information of the swelling curves to the model: Instead of predicting discrete swelling states, we predict the mathematical parameters of the continuous swelling curve. We therefore assume that the swelling behavior of hydrogels is consistent with a sigmoidal tanh function, based on their physical properties. After predicting the parameters of the sigmoidal function with the trained model, we analyze the prediction accuracy in comparison to the initial discrete prediction. Moreover, the new approach then allows us to physically interpret the different material properties, which denote sensitivity, maximum achievable differential swelling, and the position of the reference point for derivation of a material model.

Details

Original languageEnglish
Article numbere202400078
JournalProceedings in Applied Mathematics and Mechanics: PAMM
Publication statusPublished - 27 Aug 2024
Peer-reviewedNo

External IDs

unpaywall 10.1002/pamm.202400078
Mendeley 59ee2ec7-60ab-3a80-9f16-ae741c3c39a4

Keywords