  Physics-informed neural networks have gained growing interest. Specifically,
they are used to solve partial differential equations governing several
physical phenomena. However, physics-informed neural network models suffer from
several issues and can fail to provide accurate solutions in many scenarios. We
discuss a few of these challenges and the techniques, such as the use of
Fourier transform, that can be used to resolve these issues. This paper
proposes and develops a physics-informed neural network model that combines the
residuals of the strong form and the potential energy, yielding many loss terms
contributing to the definition of the loss function to be minimized. Hence, we
propose using the coefficient of variation weighting scheme to dynamically and
adaptively assign the weight for each loss term in the loss function. The
developed PINN model is standalone and meshfree. In other words, it can
accurately capture the mechanical response without requiring any labeled data.
Although the framework can be used for many solid mechanics problems, we focus
on three-dimensional (3D) hyperelasticity, where we consider two hyperelastic
models. Once the model is trained, the response can be obtained almost
instantly at any point in the physical domain, given its spatial coordinates.
We demonstrate the framework's performance by solving different problems with
various boundary conditions.
