首页|Findings from Air Force Research Laboratory in Machine Learning Reported (Physical consistency and invariance in machine learning of turbulent signals)
Findings from Air Force Research Laboratory in Machine Learning Reported (Physical consistency and invariance in machine learning of turbulent signals)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
2024 FEB 02 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on artificial intelligence are presented in a new report. According to news reporting out of Wright Patterson AFB, Ohio, by NewsRx editors, research stated, “This paper concerns an investigation of the invariance and consistency of deep learning of turbulent pressure fluctuations.” Financial supporters for this research include European Office of Aerospace Research And Development. The news correspondents obtained a quote from the research from Air Force Research Laboratory: “The long-short-memory model is employed to predict wall pressure fluctuations across physical regimes featuring turbulence, shock-boundary layer interaction, and separation. The model’s sensitivity to the data inputs is examined using different input data sets. Training the deep learning model based on the raw signals from different flow regions leads to large inaccuracies. It is shown that the data must be appropriately pre-processed before training for the deep learning model predictions to become consistent. Removing the mean and using the normalized fluctuating component of the signal, the deep learning predictions not only greatly improved in accuracy but, most importantly, converged and became consistent, provided that the signal sparsity remains within the inertial sub-range of the turbulence energy spectrum cascade. The power spectra of the surface pressure fluctuations reveal that the model provides high accuracy up to a certain frequency for the fully turbulent flow.”
Air Force Research LaboratoryWright Patterson AFBOhioUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
