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.

翻译：物理信息神经网络（PINNs）因其在解决符合物理定律的监督学习任务（尤其是非线性偏微分方程（PDEs））方面的能力而备受关注。本文介绍了“PINNs-TF2”，这是一个基于TensorFlow V2框架构建的Python软件包。它不仅能加速PINNs的实现，还通过抽象化复杂的PDE挑战简化了用户交互。我们强调了编译器在PINNs中的关键作用，指出其能够将性能提升高达119倍。通过八个不同的示例，集成了XLA编译器的软件包展示了其灵活性，并在TensorFlow V1的基础上实现了平均18.12倍的加速。此外，我们实现了一个实际案例研究，以强调编译器在处理大量可训练参数和大批量大小方面的潜力。为促进社区参与和未来改进，我们的软件包源代码已在https://github.com/rezaakb/pinns-tf2上开源。