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Using Physics Informed Neural Networks on Edge Devices: A Practical Approach
  • Nipun Agarwal
Nipun Agarwal
Birla Institute of Technology and Science

Corresponding Author:[email protected]

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This paper investigates the implementation of a physics informed neural network (PINN) using a hardware platform and hardware description language (HDL). The main objective is to investigate how real-time physics informed neural networks (PINNs) can be utilized on edge devices for digital twin applications. To make the best use of limited resources in these edge devices, they adopt a piece-wise non-linear approximation in the design of the PINN. To validate the effectiveness of their approach, they implement the PINN on a field programmable gate array (FPGA) to solve a non-linear ordinary differential equation (ODE) related to the Reynolds equation. According to the experimental findings, the hardware-based PINN achieves an impressive 95% accuracy compared to the actual solution.