Feedforward neural network-assisted parameter identification and tuning for uniaxial superelastic shape memory alloy models under dynamic loads

In this study, we introduce a machine learning-based method to predict the modeling parameters of superelastic shape memory alloys (SMAs). Our goal is to simultaneously determine and fine-tune all internal and material-related parameters, including thermodynamic ones, for a specific constitutive model using only cyclic tensile tests. We employ feedforward neural networks (FNNs) for their versatile structure. First, we sample the searched parameters within a predefined parameter space using the Latin hypercube sampling method. Then, using the constitutive model with the sampled parameters and representative strain loading, we generate the corresponding stress responses and finally train the FNN. To address the ill-posed nature of this inverse parameter identification problem and ensure a unique parameter set, during training, we use a dual network architecture with an additional FNN-based surrogate of the constitutive model. We also utilize transfer learning to accelerate the training process through knowledge transfer and handle multiple load cases simultaneously, ensuring consistent parameter identification across different scenarios. We validate the method by comparing the numerical results with the experimental data and demonstrate the importance of accurately identified parameter sets by numerical investigations on a SMA-retrofitted frame structure.