  Physics-informed neural networks (PINNs) have gained prominence for their
capability to tackle supervised learning tasks that conform to physical laws,
notably nonlinear partial differential equations (PDEs). This paper presents
"PINNs-TF2", a Python package built on the TensorFlow V2 framework. It not only
accelerates PINNs implementation but also simplifies user interactions by
abstracting complex PDE challenges. We underscore the pivotal role of compilers
in PINNs, highlighting their ability to boost performance by up to 119x. Across
eight diverse examples, our package, integrated with XLA compilers,
demonstrated its flexibility and achieved an average speed-up of 18.12 times
over TensorFlow V1. Moreover, a real-world case study is implemented to
underscore the compilers' potential to handle many trainable parameters and
large batch sizes. For community engagement and future enhancements, our
package's source code is openly available at:
https://github.com/rezaakb/pinns-tf2.
