作者：Song Minhan, Wang Wei, Sun Chong
关键字：Rubber composites, Machine learning,Physics-informed neural network,Constitutive model,Nonlinear mechanical behavior
论文来源：期刊
具体来源：Polymer 359 (2026) 130288.https://authors.elsevier.com/a/1nAnE7NHxYKmw
发表时间：2026年

This study proposes an original Physics-Informed Dual Neural Networks (PIDNN) framework to efficiently predict the complex mechanical behaviors of rubber composites under equibiaxial and planar tension, addressing a key challenge in rubber product simulation. The framework strategically integrates accessible uniaxial tensile data with physics-informed constraint from a calibrated Yeoh hyperelastic model. Through two coupled neural networks (ANN-A and ANN-B), the framework iteratively resolves complete stress-strain responses: ANN-A predicts equibiaxial stress using uniaxial test data and Yeoh-derived planar stress, while ANN-B subsequently determines planar tension stress using uniaxial and the newly predicted equibiaxial data. The PIDNN model demonstrates excellent predictive performance when validated against independent experimental datasets. Furthermore, its robustness and generalizability are successfully verified through applications to several representative rubber composites.