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Toward a general physics-informed neural network for amorphous shape memory polymer modelling.

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Title: Toward a general physics-informed neural network for amorphous shape memory polymer modelling.
Authors: Yan, Cheng1,2 (AUTHOR) cheng.yan@sus.edu, Feng, Xiaming3 (AUTHOR), Mensah, Patrick1 (AUTHOR), Li, Guoqiang2 (AUTHOR)
Source: Proceedings of the Royal Society A: Mathematical, Physical & Engineering Sciences. 7/15/2025, Vol. 481 Issue 2318, p1-24. 24p.
Subjects: Shape memory polymers, Machine learning, Phase transitions, Mechanical models, Model validation, Shape memory effect, Strains & stresses (Mechanics), Optimization algorithms
Abstract: 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. [ABSTRACT FROM AUTHOR]
Copyright of Proceedings of the Royal Society A: Mathematical, Physical & Engineering Sciences is the property of Royal Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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DbLabel: Engineering Source
An: 187258611
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Toward a general physics-informed neural network for amorphous shape memory polymer modelling.
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  Data: <searchLink fieldCode="DE" term="%22Shape+memory+polymers%22">Shape memory polymers</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Phase+transitions%22">Phase transitions</searchLink><br /><searchLink fieldCode="DE" term="%22Mechanical+models%22">Mechanical models</searchLink><br /><searchLink fieldCode="DE" term="%22Model+validation%22">Model validation</searchLink><br /><searchLink fieldCode="DE" term="%22Shape+memory+effect%22">Shape memory effect</searchLink><br /><searchLink fieldCode="DE" term="%22Strains+%26+stresses+%28Mechanics%29%22">Strains & stresses (Mechanics)</searchLink><br /><searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: 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. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Proceedings of the Royal Society A: Mathematical, Physical & Engineering Sciences is the property of Royal Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1098/rspa.2024.0702
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      – Code: eng
        Text: English
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        PageCount: 24
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      – SubjectFull: Shape memory polymers
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Phase transitions
        Type: general
      – SubjectFull: Mechanical models
        Type: general
      – SubjectFull: Model validation
        Type: general
      – SubjectFull: Shape memory effect
        Type: general
      – SubjectFull: Strains & stresses (Mechanics)
        Type: general
      – SubjectFull: Optimization algorithms
        Type: general
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      – TitleFull: Toward a general physics-informed neural network for amorphous shape memory polymer modelling.
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            NameFull: Yan, Cheng
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            NameFull: Feng, Xiaming
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            NameFull: Mensah, Patrick
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            NameFull: Li, Guoqiang
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            – D: 15
              M: 07
              Text: 7/15/2025
              Type: published
              Y: 2025
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              Value: 481
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            – TitleFull: Proceedings of the Royal Society A: Mathematical, Physical & Engineering Sciences
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