Practical sparse data-driven constitutive modeling via transfer learning in physics-encoded neural networks.

ABSTRACT: Data-driven constitutive models, owing to their inherent flexibility, can outperform traditional plasticity-based models in certain aspects. When calibrating these models, ensuring adherence to fundamental mechanical principles allows the calibrated models, referred to as physics-encoded neural networks (PeNNs), to be effectively integrated into finite element method (FEM) software for boundary value problem simulations. However, calibration challenges arise when only limited data are available. Addressing this issue, this study employs transfer learning. Synthetic labeled data, derived from traditional constitutive models were used to pre-train PeNNs. Subsequently, these pre-trained PeNNs are fine-tuned using implicitly labeled data from high-fidelity experimental records. The fine-tuned

SUBMITTER: Wang Z 

PROVIDER: S-EPMC12775455 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature