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Physics-informed neural networks (PINNs) have gained prominence for theircapability 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 onlyaccelerates PINNs implementation but also simplifies user interactions byabstracting complex PDE challenges. We underscore the pivotal role of compilersin PINNs, highlighting their ability to boost performance by up to 119x. Acrosseight diverse examples, our package, integrated with XLA compilers,demonstrated its flexibility and achieved an average speed-up of 18.12 timesover TensorFlow V1. Moreover, a real-world case study is implemented tounderscore the compilers' potential to handle many trainable parameters andlarge batch sizes. For community engagement and future enhancements, ourpackage's source code is openly available at:https://github.com/rezaakb/pinns-tf2.

PINNs-TF2: Fast and User-Friendly Physics-Informed Neural Networks in TensorFlow V2