Multi-fidelity prediction of fluid flow and temperature field based on transfer learning using Fourier Neural Operator. (arXiv:2304.06972v1 [physics.flu-dyn])

Data-driven prediction of fluid flow and temperature distribution in marine
and aerospace engineering has received extensive research and demonstrated its
potential in real-time prediction recently. However, usually large amounts of
high-fidelity data are required to describe and accurately predict the complex
physical information, while in reality, only limited high-fidelity data is
available due to the high experiment/computational cost. Therefore, this work
proposes a novel multi-fidelity learning method based on the Fourier Neural
Operator by jointing abundant low-fidelity data and limited …

aerospace arxiv computational cost data data-driven distribution engineering experiment fidelity flow information low marine novel physics prediction reality real-time research transfer transfer learning work