Modeling elastoviscoplastic materials using physics-informed neural networks

Abstract

Elastoviscoplastic (EVP) materials pose significant challenges for predictive modeling, especially under large amplitude oscillatory shear (LAOS) conditions due to their nonlinear rheological behavior. While various models have been proposed to describe yielding and viscoelastic transitions, their application to experimental data remains limited due to computational complexity, poor generalizability, and difficulty in fitting noisy data. This work re-evaluates rheological modeling of EVP materials by using a physics-informed neural network (PINN) approach that embeds a modified Saramito model into the training framework. By directly fitting time-dependent stress data on typical EVP materials, this method circumvents the need for gradient estimation from noisy measurements and offers a data-efficient, differentiable, and generalizable alternative to conventional fitting methods. A shear-thinning formulation is introduced to reflect the decreasing viscosity at higher strain amplitudes. The proposed PINN approach is validated using both synthetic and experimental data, demonstrating stable recovery of physical parameters, enhanced interpretability, and improved predictive capability across a range of strain amplitudes. This framework bridges microstructural deformation modes with rheological modeling, offering a powerful tool for understanding and predicting nonlinear viscoelastic behavior in soft matter.