Enhancement of PIV measurements via physics-informed neural networks

This repository features codes and data employed for developing physics-informed neural networks (PINNs) for enhancing boundary layer PIV measurements by reconstructing the mean velocity field and correcting the measurement errors in the data. Within this framework, the PINNs-based model solves the Reynolds-averaged Navier–Stokes equations for turbulent boundary layers without a prior assumption and by only taking the data at the domain boundaries. More details about the implementation and the results are available in Hasanuzzaman G, Eivazi H, Merbold S, Egbers C, Vinuesa R., 2023. Enhancement of PIV measurements via physics-informed neural networks. Meas. Sci. Technol. 34 044002.

Citation

@article{Hasanuzzaman_2023,
  author = {Gazi Hasanuzzaman and Hamidreza Eivazi and Sebastian Merbold and Christoph Egbers and Ricardo Vinuesa},
  title = {Enhancement of PIV measurements via physics-informed neural networks},
  year = {2023},
  publisher = {IOP Publishing},
  volume = {34},
  number = {4},
  pages = {044002},
  journal = {Measurement Science and Technology},
  doi = {10.1088/1361-6501/aca9eb},
}