Computer Science > Computational Engineering, Finance, and Science
[Submitted on 7 Oct 2020 (v1), last revised 25 Oct 2021 (this version, v5)]
Title:Calibration of Elastoplastic Constitutive Model Parameters from Full-field Data with Automatic Differentiation-based Sensitivities
View PDFAbstract:We present a framework for calibration of parameters in elastoplastic constitutive models that is based on the use of automatic differentiation (AD). The model calibration problem is posed as a partial differential equation-constrained optimization problem where a finite element (FE) model of the coupled equilibrium equation and constitutive model evolution equations serves as the constraint. The objective function quantifies the mismatch between the displacement predicted by the FE model and full-field digital image correlation data, and the optimization problem is solved using gradient-based optimization algorithms. Forward and adjoint sensitivities are used to compute the gradient at considerably less cost than its calculation from finite difference approximations. Through the use of AD, we need only to write the constraints in terms of AD objects, where all of the derivatives required for the forward and inverse problems are obtained by appropriately seeding and evaluating these quantities. We present three numerical examples that verify the correctness of the gradient, demonstrate the AD approach's parallel computation capabilities via application to a large-scale FE model, and highlight the formulation's ease of extensibility to other classes of constitutive models.
Submission history
From: Daniel Seidl [view email][v1] Wed, 7 Oct 2020 20:57:15 UTC (3,812 KB)
[v2] Wed, 2 Dec 2020 21:07:28 UTC (3,165 KB)
[v3] Tue, 18 May 2021 16:05:21 UTC (7,170 KB)
[v4] Thu, 15 Jul 2021 21:28:55 UTC (6,043 KB)
[v5] Mon, 25 Oct 2021 13:04:38 UTC (6,043 KB)
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