  The research in Artificial Intelligence methods with potential applications
in science has become an essential task in the scientific community last years.
Physics Informed Neural Networks (PINNs) is one of this methods and represent a
contemporary technique that is based on the fundamentals of neural networks to
solve differential equations. These kind of networks have the potential to
improve or complement classical numerical methods in computational physics,
making them an exciting area of study. In this paper, we introduce PINNs at an
elementary level, mainly oriented to physics education so making them suitable
for educational purposes at both undergraduate and graduate levels. PINNs can
be used to create virtual simulations and educational tools that aid in
understating complex physical concepts and processes where differential
equations are involved. By combining the power of neural networks with physics
principles, PINNs can provide an interactive and engaging learning experience
that can improve students' understanding and retention of physics concepts in
higher education.
