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In the age of big data, the Internet big data can finely reflect public attention to air pollution, which greatly impact ambient PM2.5 concentrations; however, it has not been applied to PM2.5 prediction yet. Therefore, this study introduces such informative Internet big data as an effective predictor for PM2.5, in addition to other big data. To capture the multi-scale relationship between PM2.5 concentrations and multi-source big data, a novel multi-source big data and multi-scale forecasting methodology is proposed for PM2.5. Three major steps are taken: 1) Multi-source big data process, to collect big data from different sources (e.g., devices and Internet) and extract the hidden predictive features; 2) Multi-scale analysis, to address the non-uniformity and nonalignment of timescales by withdrawing the scale-aligned modes hidden in multi-source data; 3) PM2.5 prediction, entailing individual prediction at each timescale and ensemble prediction for the final results. The empirical study focuses on the top highly-polluted cities and shows that the proposed multi-source big data and multi-scale forecasting method outperforms its original forms (with neither big data nor multi-scale analysis), semi-extended variants (with big data and without multi-scale analysis) and similar counterparts (with big data but from a single source and multi-scale analysis) in accuracy. [ABSTRACT FROM AUTHOR]

Numerical simulation in Computational Fluid Dynamics mainly relies on discretizing the governing equations in time or space to obtain numerical solutions, which is expensive and time-consuming. Deep learning significantly reduces the computational cost due to its great nonlinear curve fitting capability, however, the data-driven models is agnostic to latent relationships between input and output. In this paper, a novel deep learning named Navier–Stokes Generative Adversarial Network integrated with physical information is proposed. The Navier–Stokes Equation is added to the loss function of the generator in the form of residuals, which means physics loss in this paper. Then, the proposed model is trained in the framework of generative adversarial network. Experimental results show that proposed model significantly outperform similar models, mean absolute error are decreased by 62.29, 78.42 and 78.61% on pressure and velocity components. What’s more, effectiveness of introducing physics loss is also verified. [ABSTRACT FROM AUTHOR]