  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.
