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A Physics-Informed Manifold Neural Operator Framework for Multi-Parameter Prediction of Polymer Aging in HTPB Solid Propellants.

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Title: A Physics-Informed Manifold Neural Operator Framework for Multi-Parameter Prediction of Polymer Aging in HTPB Solid Propellants.
Authors: Liu, Shun1 (AUTHOR), Qiang, Hongfu1 (AUTHOR), Geng, Tingjing1 (AUTHOR), Wang, Xueren1 (AUTHOR) rocketwangxr@126.com, Pei, Shudi1 (AUTHOR), Ju, Xin1 (AUTHOR)
Source: Polymers (20734360). Jun2026, Vol. 18 Issue 11, p1400. 26p.
Subjects: Solid propellants, Crosslinking (Polymerization), Machine learning, Viscoelasticity, Prediction models, Polymer degradation
Abstract: Predictive modeling of thermal aging in solid propellants is challenging because HTPB-based propellants are highly filled particle-reinforced polymer systems with sparse experimental data, nonlinear parameter coupling, and partially unclear aging mechanisms. This study proposes a Physics-Informed Manifold Neural Operator (PIMANO) framework for multi-parameter prediction of polymer aging in HTPB solid propellants. Accelerated thermal aging, stress relaxation, and swelling experiments were used to obtain aging temperature, aging time, crosslinking density, and viscoelastic Prony-series parameters. A continuous aging-state field was first reconstructed over the temperature–time domain by radial basis function interpolation. Crosslinking density was then introduced as a physically interpretable bridge-state variable linking aging conditions with viscoelastic responses. Among three candidate kinetic models, the modified Arrhenius–Avrami model gave the best fitting performance for crosslinking-density evolution, with R2 = 0.988 and MRE = 0.0199. By combining local multi-scale neighborhood features, manifold latent representations, and DeepONet-based operator learning, PIMANO established a unified mapping from aging conditions to multi-parameter viscoelastic responses while incorporating bridge-state consistency, parameter non-negativity, and evolution-direction constraints. Under the RBF-augmented validation setting, PIMANO-ae achieved RMSE = 0.7847, MAE = 0.3366, R2 = 0.9995, and MRE = 0.0027. Compared with the traditional model, RMSE, MAE, and MRE were reduced by 94.93%, 96.47%, and 96.85%, respectively. Temperature leave-one-out validation further yielded average R2 values of 0.9469–0.9647 and MRE values of 4.98–6.21% at unseen aging temperatures. These results demonstrate that PIMANO provides an accurate, stable, and physically interpretable framework for multi-parameter aging prediction and life-assessment modeling of polymer-based energetic materials. [ABSTRACT FROM AUTHOR]
Copyright of Polymers (20734360) is the property of MDPI 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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  Data: A Physics-Informed Manifold Neural Operator Framework for Multi-Parameter Prediction of Polymer Aging in HTPB Solid Propellants.
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  Data: <searchLink fieldCode="JN" term="%22Polymers+%2820734360%29%22">Polymers (20734360)</searchLink>. Jun2026, Vol. 18 Issue 11, p1400. 26p.
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  Data: <searchLink fieldCode="DE" term="%22Solid+propellants%22">Solid propellants</searchLink><br /><searchLink fieldCode="DE" term="%22Crosslinking+%28Polymerization%29%22">Crosslinking (Polymerization)</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Viscoelasticity%22">Viscoelasticity</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Polymer+degradation%22">Polymer degradation</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Predictive modeling of thermal aging in solid propellants is challenging because HTPB-based propellants are highly filled particle-reinforced polymer systems with sparse experimental data, nonlinear parameter coupling, and partially unclear aging mechanisms. This study proposes a Physics-Informed Manifold Neural Operator (PIMANO) framework for multi-parameter prediction of polymer aging in HTPB solid propellants. Accelerated thermal aging, stress relaxation, and swelling experiments were used to obtain aging temperature, aging time, crosslinking density, and viscoelastic Prony-series parameters. A continuous aging-state field was first reconstructed over the temperature–time domain by radial basis function interpolation. Crosslinking density was then introduced as a physically interpretable bridge-state variable linking aging conditions with viscoelastic responses. Among three candidate kinetic models, the modified Arrhenius–Avrami model gave the best fitting performance for crosslinking-density evolution, with R2 = 0.988 and MRE = 0.0199. By combining local multi-scale neighborhood features, manifold latent representations, and DeepONet-based operator learning, PIMANO established a unified mapping from aging conditions to multi-parameter viscoelastic responses while incorporating bridge-state consistency, parameter non-negativity, and evolution-direction constraints. Under the RBF-augmented validation setting, PIMANO-ae achieved RMSE = 0.7847, MAE = 0.3366, R2 = 0.9995, and MRE = 0.0027. Compared with the traditional model, RMSE, MAE, and MRE were reduced by 94.93%, 96.47%, and 96.85%, respectively. Temperature leave-one-out validation further yielded average R2 values of 0.9469–0.9647 and MRE values of 4.98–6.21% at unseen aging temperatures. These results demonstrate that PIMANO provides an accurate, stable, and physically interpretable framework for multi-parameter aging prediction and life-assessment modeling of polymer-based energetic materials. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Polymers (20734360) is the property of MDPI 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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        Value: 10.3390/polym18111400
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 26
        StartPage: 1400
    Subjects:
      – SubjectFull: Solid propellants
        Type: general
      – SubjectFull: Crosslinking (Polymerization)
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Viscoelasticity
        Type: general
      – SubjectFull: Prediction models
        Type: general
      – SubjectFull: Polymer degradation
        Type: general
    Titles:
      – TitleFull: A Physics-Informed Manifold Neural Operator Framework for Multi-Parameter Prediction of Polymer Aging in HTPB Solid Propellants.
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            NameFull: Liu, Shun
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            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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