A Direct-adjoint Approach for Material Point Model Calibration with Application to Plasticity
Abstract
This paper proposes a new approach for the calibration of material parameters in local elastoplastic constitutive models. The calibration is posed as a constrained optimization problem, where the constitutive model evolution equations for a single material point serve as constraints. The objective function quantifies the mismatch between the stress predicted by the model and corresponding experimental measurements. To improve calibration efficiency, a novel direct-adjoint approach is presented to compute the Hessian of the objective function, which enables the use of second-order optimization algorithms. Automatic differentiation is used for gradient and Hessian computations. Two numerical examples are employed to validate the Hessian matrices and to demonstrate that the Newton-Raphson algorithm consistently outperforms gradient-based algorithms such as L-BFGS-B.
Cite
@article{arxiv.2501.04584,
title = {A Direct-adjoint Approach for Material Point Model Calibration with Application to Plasticity},
author = {Ryan Yan and D. Thomas Seidl and Reese E. Jones and Panayiotis Papadopoulos},
journal= {arXiv preprint arXiv:2501.04584},
year = {2025}
}