»ã±¨Ö÷Ì⣺Model-based Data Assimilation versus Data-driven Machine Learning(»ùÓÚÄ£Ð͵ÄÊý¾Ýͬ»¯ÓëÊý¾ÝÇý¶¯µÄ»úе½ø½¨£©

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»áÒéÃÜÂ룺202028

»áÒéµØÖ·:https://meeting.tencent.com/s/Y581PQnWVDxf

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»ã±¨ÌáÒª£ºUncertainty is common in real life, both mathematical-physical models and observations contain uncertainties. Data assimilation is a method which uses the information of observation data to reduce the uncertainty in the model consequently improving the forecast accuracy of the model. Machine learning is a data-driven method which tries to find the important features and their relations from the data, in contrast to model-based data assimilation, machine learning techniques do not require a mathematical-physical model and try to fit the data into some functional relationship through an optimization procedure. In this sense machine learning is therefore an ¡°interpolation¡± method without paying attention to ¡°extrapolation¡±. Combining the power of the model-based data assimilation method and the data-driven machine learning technique is the focus of many recent research, in this talk we will discuss some examples of this development.

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