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Exploration of deep operator networks for predicting the piezoionic effect.

Shuyu Wang^1,2, Dingli Zhang¹, Ao Wang¹

¹School of Control Engineering, Northeastern University at Qinhuangdao Campus, Qinhuangdao 066000, China.

The Journal of Chemical Physics

|March 17, 2025

Summary

This summary is machine-generated.

This study introduces deep operator networks to model the complex piezoionic effect, significantly reducing computation time for biomedical electronics and ionic skins. The data-driven approach accurately captures temporal dynamics for real-time analysis.

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Area of Science:

Multiphysics phenomena
Biomedical electronics
Ionic skins

Background:

Modeling the piezoionic effect is complex and computationally intensive.
Existing methods like finite element analysis (FEA) face computational limitations.
Accurate modeling is crucial for advancing biomedical electronics and ionic skins.

Purpose of the Study:

To pioneer the application of deep operator networks (DeepONet) for modeling the time-dependent piezoionic effect.
To develop a data-driven model that overcomes the computational challenges of traditional methods.
To enable real-time analysis of piezoionic phenomena.

Main Methods:

Utilized deep operator networks (DeepONet) trained on a comprehensive dataset.
Dataset generated via finite element analysis (FEA) calibrated with experimental data.
Validated the model using step, slow-changing, and dynamic-changing force responses.

Main Results:

The DeepONet model effectively captures the temporal dynamics of the piezoionic effect.
Demonstrated accurate modeling in both horizontal and vertical planes.
Achieved significant reduction in computational time compared to FEA.

Conclusions:

Deep operator networks provide an effective and computationally efficient solution for modeling the piezoionic effect.
The developed model facilitates real-time analysis, aiding in the design of tactile interfaces.
This approach has the potential to complement existing tactile imaging technologies.