Blar i UiS Brage på forfatter "Li, Qing"
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Identification of the flux function of nonlinear conservation laws with variable parameters
Li, Qing; Geng, Jiahui; Evje, Steinar (Peer reviewed; Journal article, 2023-09)Machine learning methods have in various ways emerged as a useful tool for modeling the dynamics of physical systems in the context of partial differential equations (PDEs). Nonlinear conservation laws (NCLs) of the form ... -
Learning Parameterized ODEs From Data
Li, Qing; Evje, Steinar; Geng, Jiahui (Peer reviewed; Journal article, 2023)In contemporary research, neural networks are being used to derive Ordinary Differential Equations (ODEs) from observations. However, parameterized ODEs pose a more significant challenge than non-parameterized ODEs since ... -
Learning the nonlinear flux function of a hidden scalar conservation law from data
Li, Qing; Evje, Steinar (Peer reviewed; Journal article, 2023-10)Nonlinear conservation laws are widely used in fluid mechanics, biology, physics, and chemical engineering. However, deriving such nonlinear conservation laws is a significant and challenging problem. A possible attractive ... -
On the numerical discretization of a tumor progression model driven by competing migration mechanisms
Qiao, Yangyang; Li, Qing; Evje, Steinar (Peer reviewed; Journal article, 2021)In this work we explore a recently proposed biphasic cell-fluid chemotaxis-Stokes model which is able to represent two competing cancer cell migration mechanisms reported from experimental studies. Both mechanisms depend ... -
Solving Nonlinear Conservation Laws of Partial Differential Equations Using Graph Neural Networks
Li, Qing; Geng, Jiahui; Evje, Steinar; Rong, Chunming (Peer reviewed; Journal article, 2023)Nonlinear Conservation Laws of Partial Differential Equations (PDEs) are widely used in different domains. Solving these types of equations is a significant and challenging task. Graph Neural Networks (GNNs) have recently ...