Reconstructing turbulent velocity and pressure fields from under-resolved noisy particle tracks using physics-informed neural networks. Clark Di Leoni, P., Agarwal, K., Zaki, T., A., Meneveau, C., & Katz, J. Experiments in Fluids, 64(5):95, Springer, 2023.
bibtex   
@article{
 title = {Reconstructing turbulent velocity and pressure fields from under-resolved noisy particle tracks using physics-informed neural networks},
 type = {article},
 year = {2023},
 pages = {95},
 volume = {64},
 publisher = {Springer},
 id = {9291fe32-5936-3f21-94cb-15258cd0415d},
 created = {2023-06-10T00:46:54.118Z},
 file_attached = {false},
 profile_id = {75799766-8e2d-3c98-81f9-e3efa41233d0},
 group_id = {c9329632-2a50-3043-b803-cadc8dbdfc3f},
 last_modified = {2023-06-10T00:46:54.118Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 source_type = {article},
 private_publication = {false},
 bibtype = {article},
 author = {Clark Di Leoni, Patricio and Agarwal, Karuna and Zaki, Tamer A and Meneveau, Charles and Katz, Joseph},
 journal = {Experiments in Fluids},
 number = {5}
}

Downloads: 0