  The identification of material parameters occurring in constitutive models
has a wide range of applications in practice. One of these applications is the
monitoring and assessment of the actual condition of infrastructure buildings,
as the material parameters directly reflect the resistance of the structures to
external impacts. Physics-informed neural networks (PINNs) have recently
emerged as a suitable method for solving inverse problems. The advantages of
this method are a straightforward inclusion of observation data. Unlike
grid-based methods, such as the least square finite element method (LS-FEM)
approach, no computational grid and no interpolation of the data is required.
In the current work, we propose PINNs for the calibration of constitutive
models from full-field displacement and global force data in a realistic regime
on the example of linear elasticity. We show that conditioning and
reformulation of the optimization problem play a crucial role in real-world
applications. Therefore, among others, we identify the material parameters from
initial estimates and balance the individual terms in the loss function. In
order to reduce the dependency of the identified material parameters on local
errors in the displacement approximation, we base the identification not on the
stress boundary conditions but instead on the global balance of internal and
external work. We demonstrate that the enhanced PINNs are capable of
identifying material parameters from both experimental one-dimensional data and
synthetic full-field displacement data in a realistic regime. Since
displacement data measured by, e.g., a digital image correlation (DIC) system
is noisy, we additionally investigate the robustness of the method to different
levels of noise.
