Abstract
Investigators discuss new findings in Machine Learning. According to news originating from Harbin, People's Republic o f China, by NewsRx correspondents, research stated, "The real-time prediction of flow fields has scientific and engineering significance, although it is current ly challenging. To address this issue, we propose a nonintrusive supervised redu ced-order machine learning framework for flow-field reconstruction, referred to as ROR, to achieve real-time flow-field prediction." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). Our news journalists obtained a quote from the research from the Harbin Institut e of Technology, "The model predicts a signed distance function of the domain an d uses a typical flow field as feature extraction objects. Utilizing a cross-fit method, it efficiently combines these features, enabling rapid prediction of th e full-order flow field. During the model validation phase, we assess the perfor mance of our model by reconstructing steady-state two-dimensional indoor flows i n different room layouts. The results indicate that our model accurately predict s the flow field in the target indoor layout within a short timeframe (approxima tely 5 s) and demonstrates robustness. To delve deeper into the model performanc e, we discuss the specific parameters of the model framework and test the effect iveness of the flow-field reconstruction under different air supply modes, with the results showing a mean squared error (MSE) of less than 1.5 %. Additionally, we compare our model with the fourier neural operator (FNO) model and find that it exhibited superior performance with the same number of training steps."
NETL
NSTL