 | | Mechanics of Solids
A Journal of Russian Academy of Sciences | | Founded
in January 1966
Issued 6 times a year
Print ISSN 0025-6544
Online ISSN 1934-7936 |
Archive of Issues
| Total articles in the database: | | 13362 |
| In Russian (Èçâ. ÐÀÍ. ÌÒÒ): | | 8178
|
| In English (Mech. Solids): | | 5184 |
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| Hengdi Su, Feifei Song, Xiaolong Zhang, and Huixian Yan, "Physics-Constrained Neural Network with Inverse Transform Sampling for Temperature-Humidity Coupling Effects on Cavitation in Thermo-Responsive Elastomeric Gels," Mech. Solids. 60 (5), 3846-3863 (2025) |
| Year |
2025 |
Volume |
60 |
Number |
5 |
Pages |
3846-3863 |
| DOI |
10.1134/S0025654425602101 |
| Title |
Physics-Constrained Neural Network with Inverse Transform Sampling for Temperature-Humidity Coupling Effects on Cavitation in Thermo-Responsive Elastomeric Gels |
| Author(s) |
Hengdi Su (College of Information and Management Science, Henan Agricultural University, Zhengzhou, 450002 China)
Feifei Song (College of Information and Management Science, Henan Agricultural University, Zhengzhou, 450002 China, feifeisong2013@163.com)
Xiaolong Zhang (Shanghai Aerospace Control Technology Institute, Shanghai, 201109 China)
Huixian Yan (School of Mechanical and Electrical Engineering, Sanming University, Sanming, 365004 China, yanhx1982@126.com) |
| Abstract |
This work presents a novel computational framework for analyzing cavitation phenomena
triggered by environmental temperature and humidity in thermo-responsive elastomeric gels.
We demonstrate that cavitation instabilities, originating from pre-existing defects, exhibit discontinuous cavity expansion under permeable boundary constraints in swollen gels. Through variational
methods, constitutive equations are achieved based on equilibrium thermodynamics of elastomeric
gels. Physics-constrained neural network with inverse transform sampling (ITS-PCNN) is developed
to approximate the solution to the governing equation, which demonstrates superior stability and
accuracy compared to conventional physics-informed neural networks (PINN), achieving consistently lower residual norms for both geometric and equilibrium equations. The ITS-PCNN framework enables comprehensive investigation of the coupling effects of temperature and humidity on cavitation behavior in thermo-responsive elastomeric gels. Our findings establish a robust computational
platform for predicting cavitation responses under varying environmental conditions, advancing the
design of responsive materials for biomedical and microfluidic applications. |
| Keywords |
Thermo-responsive elastomeric gels, Cavitation instability, Physics-constrained neural network, Inverse transform sampling, Temperature-humidity coupling |
| Received |
28 April 2025 | Revised |
02 July 2025 | Accepted |
03 July 2025 |
| Link to Fulltext |
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