Polymer Rheology

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• Published Subscription Original Research 25 Jul 2026

  Hybrid Quantum–Machine Learning Framework for Nonlinear Rheological Modeling of Polymer and Composite Materials

  By Pijush Dutta, Pradip Deb, Pooja Nagpal, Shantanu Naskar, Anubrata Mondal, Tan Yi Fei, Chua Fang

  Abstract: In polymer and composite materials, a major challenge lies in predicting their nonlinear rheological response, owing to complex multiscale interactions that are not captured by traditional constitutive laws or conventional machine learning approaches. In this study, a hybrid Quantum Machine Learning (QML) model comprising Quantum Support Vector Machine (QSVM) and Quantum Neural Network (QNN) architectures is proposed for viscosity prediction without requiring any specific rheological equation. To train and test …

  Published in Journal of Polymer & Composites · Vol. 14, Issue 5, 2026 Read article →

• Published Subscription Original Research 3 Jul 2026

  Study of an Improved Quantum Particle Swarm Optimization-Based Framework for Neural Network Optimization in Modelling of Polymer Data

  By Kakali Das, Amrita Mitra, Sagardeep Ghosh, Suchismita Maiti, Soumyabrata Saha, Pushpita Roy, Anubrata Mondal, Pijush Dutta

  Abstract: The accurate forecasting of polymer viscosity at various physicochemical conditions has been quite critical due to the nonlinear interactions and interrelations between the variables. This paper suggests a better hybrid modelling framework, which involves the use of Artificial Neural Networks (ANN) and more advanced versions of Quantum Particle Swarm Optimization (QPSO) to better predict polymer viscosity. The input parameters taken are, namely, log (shear rate), polymer concentration, NaCl concentration, Ca …

  Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article →