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Shape memory polymers (SMPs) are a new member of the smart materials family. SMPs have found wide applications or potential applications in almost all manmade structures and devices. In order to better design smart structures and devices using SMPs, thermomechanical constitutive modeling is essential. In this insight paper, we will focus on presenting several multi-length-scale and multi-physics modeling frameworks, including the thermodynamics consistent model, elasto-viscoplastic model, statistical mechanics model, and phase evaluation law model. The SMPs modeled will include amorphous one-way shape memory polymers, semicrystalline one-way shape memory polymers, semicrystalline two-way shape memory polymers, and functional and mechanical damage effects on SMPs. Finally, we will give some in-depth perspectives on future development in this area of study.
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This content will become publicly available on July 1, 2026
Toward a general physics-informed neural network for amorphous shape memory polymer modelling
Due to the complex behaviour of amorphous shape memory polymers (SMPs), traditional constitutive models often struggle with material-specific limitations, challenging curve-fitting, history-dependent stress calculations and error accumulation from stepwise calculation for governing equations. In this study, we propose a physics-informed artificial neural network (PIANN) that integrates a conventional neural network with a strain-based phase transition framework to predict the constitutive behaviour of amorphous SMPs. The model is validated using five temperature–stress datasets and four temperature–strain datasets, including experimental data from four types of SMPs and simulation results from a widely accepted model. PIANN predicts four key shape memory behaviours: stress evolution during hot programming, stress recovery following both cold and hot programming and free strain recovery during heating branch. Notably, it predicts recovery strain during heating without using any heating data for training. Comparisons with experimental data show excellent agreement in both programming (cooling) and recovery (heating) branches. Remarkably, the model achieves this performance with as few as two temperature–stress curves in the training set. Overall, PIANN addresses common challenges in SMP modelling by eliminating history dependence, improving curve-fitting accuracy and significantly enhancing computational efficiency. This work represents a substantial step forward in developing generalizable models for SMPs.
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- Award ID(s):
- 2418415
- PAR ID:
- 10615968
- Publisher / Repository:
- Royal Society
- Date Published:
- Journal Name:
- Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
- Volume:
- 481
- Issue:
- 2318
- ISSN:
- 1364-5021
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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