A physics-informed neural network pipeline reproduces the known soliton family of the internal-wave KdV equation and recovers layer parameters from sparse synthetic observations when one density is fixed.
PINNs-TF2: Fast and User-Friendly Physics-Informed Neural Networks in TensorFlow V2
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abstract
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.
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physics.flu-dyn 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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Physics-Informed Neural Networks for the Korteweg-de Vries Equation for Internal Solitary Wave Problem: Forward Simulation and Inverse Parameter Estimation
A physics-informed neural network pipeline reproduces the known soliton family of the internal-wave KdV equation and recovers layer parameters from sparse synthetic observations when one density is fixed.