  Deep learning has been highly successful in some applications. Nevertheless,
its use for solving partial differential equations (PDEs) has only been of
recent interest with current state-of-the-art machine learning libraries, e.g.,
TensorFlow or PyTorch. Physics-informed neural networks (PINNs) are an
attractive tool for solving partial differential equations based on sparse and
noisy data. Here extend PINNs to solve obstacle-related PDEs which present a
great computational challenge because they necessitate numerical methods that
can yield an accurate approximation of the solution that lies above a given
obstacle. The performance of the proposed PINNs is demonstrated in multiple
scenarios for linear and nonlinear PDEs subject to regular and irregular
obstacles.
