Dataset of 'Complete flow characterization from snapshot PIV, fast probes and physics-informed neural networks'
Dataset of 'Complete flow characterization from snapshot PIV, fast probes and physics-informed neural networks'
Dataset of the article 'Complete flow characterization from snapshot PIV, fast probes and physics-informed neural networks' (https://doi.org/10.1016/j.cma.2023.116652). The codes processing data here are on https://github.com/AlvaroMS90/Complete-flow-characterization-from-snapshot-PIV-fast-probes-and-physics-informed-neural-networks. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No 949085) and by MCIN/AEI /10.13039/501100011033 and the European Union ‘NextGenerationEU/PRTR’ as part of the grant FJC2020-044342-I.
Flow estimation, Physics-informed neural networks, Proper orthogonal decomposition, Particle image velocimetry
Flow estimation, Physics-informed neural networks, Proper orthogonal decomposition, Particle image velocimetry
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Citations provided by BIP!Popularity provided by BIP!