  Material identification is critical for understanding the relationship
between mechanical properties and the associated mechanical functions. However,
material identification is a challenging task, especially when the
characteristic of the material is highly nonlinear in nature, as is common in
biological tissue. In this work, we identify unknown material properties in
continuum solid mechanics via physics-informed neural networks (PINNs). To
improve the accuracy and efficiency of PINNs, we developed efficient strategies
to nonuniformly sample observational data. We also investigated different
approaches to enforce Dirichlet boundary conditions as soft or hard
constraints. Finally, we apply the proposed methods to a diverse set of
time-dependent and time-independent solid mechanic examples that span linear
elastic and hyperelastic material space. The estimated material parameters
achieve relative errors of less than 1%. As such, this work is relevant to
diverse applications, including optimizing structural integrity and developing
novel materials.
