Salvato in:
| Autori principali: | , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2501.04584 |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866913825941356544 |
|---|---|
| author | Yan, Ryan Seidl, D. Thomas Jones, Reese E. Papadopoulos, Panayiotis |
| author_facet | Yan, Ryan Seidl, D. Thomas Jones, Reese E. Papadopoulos, Panayiotis |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_04584 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Direct-adjoint Approach for Material Point Model Calibration with Application to Plasticity Yan, Ryan Seidl, D. Thomas Jones, Reese E. Papadopoulos, Panayiotis Computational Engineering, Finance, and Science 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. |
| title | A Direct-adjoint Approach for Material Point Model Calibration with Application to Plasticity |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2501.04584 |