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. We envision a research paradigm shift in fluid mechanics to a physics-informed (and -informative) probabilistic learning framework, which leads to disruptive technology transformation in the aerospace
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. We envision a research paradigm shift in fluid mechanics to a physics-informed (and -informative) probabilistic learning framework, which leads to disruptive technology transformation in the aerospace
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. Liying Li, Associate Professor, Research Interests: Probability, Ergodic theory on stochastic processes, SPDE, KPZ-related probabilistic models, Email: lily@sustech.edu.cn. 4. Kailiang Wu, Associate
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journals.Research experience on Statistical modeling (Regression, Classification, Time-series forecasting) /Machine Learning /Deep Learning/Text Mining /Optimization /Visualization are desirable. Academic rank will
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and Sales Management, Business to Business or Industrial Marketing, Service Management and Marketing, Marketing Research, Marketing in China, Business Forecasting and Machine Learning, Deep Learning