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Robust PDE Identification from a Noisy Data Set
Partial differential equation (PDE) is an important tool to describe physical laws in many disciplines. Traditional ways in discovering PDEs from data sets requires a lot of time. As the advances of technology, large amounts of data are easy to collect and store, which provides new opportunities for data-driven identification of PDE. This presentation addresses our recent work on identifying PDEs from a given data set. In PDE identification, many existing methods cannot deal with data with heavy noise. We propose a new strategy to denoise the data and compute partial derivatives with improved accuracy. We also propose sparse regression based methods that can efficiently identify the underlying PDE from data with large noise.
Dr. Hao LIU is an assistant professor in Hong Kong Baptist University. Dr. Liu received the Bachelor degree in applied and computational mathematics from Hong Kong Baptist University in 2014, and the Ph.D. degree in applied mathematics from The Hong Kong University of Science and Technology in 2018. Before joined HKBU, he was a postdoc at Georgia Institute of Technology.
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