Q
Qizhi He
Researcher at Pacific Northwest National Laboratory
Publications - 22
Citations - 623
Qizhi He is an academic researcher from Pacific Northwest National Laboratory. The author has contributed to research in topics: Artificial neural network & Finite element method. The author has an hindex of 7, co-authored 22 publications receiving 300 citations. Previous affiliations of Qizhi He include Dalian University of Technology & Wuhan University.
Papers
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Journal ArticleDOI
Physics-Informed Neural Networks for Multiphysics Data Assimilation with Application to Subsurface Transport
TL;DR: It is demonstrated that the physics-informed deep neural networks used for estimating space-dependent hydraulic conductivity, hydraulic head, and concentration fields from sparse measurements are significantly more accurate than standard data-driven DNNs when the training set consists of sparse data.
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An adaptive refinement approach for topology optimization based on separated density field description
Yiqiang Wang,Zhan Kang,Qizhi He +2 more
TL;DR: This method is able to improve the boundary description quality of the optimal result with much less design variables as compared with the case of global refinement, and therefore can greatly reduce the computational burden involved in the sensitivity analysis and optimization process.
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Adaptive topology optimization with independent error control for separated displacement and density fields
Yiqiang Wang,Zhan Kang,Qizhi He +2 more
TL;DR: In this article, an adaptive method for topology optimization of structures, by using independent error control for the separated displacement and material density fields, is proposed, which can achieve high quality and high-accuracy optimal solutions comparable to those obtained with fixed globally fine analysis meshes and fine distributed density points, but with much less computational cost.
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A physics-constrained data-driven approach based on locally convex reconstruction for noisy database
Qizhi He,Jiun-Shyan Chen +1 more
TL;DR: In this paper, a new data-driven simulation approach coupled with a locally convex reconstruction is proposed to enhance accuracy and robustness against noise and outliers in data sets in the data driven computing.
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A topology optimization method for geometrically nonlinear structures with meshless analysis and independent density field interpolation
Qizhi He,Zhan Kang,Yiqiang Wang +2 more
TL;DR: Based on the element-free Galerkin (EFG) method, an analysis-independent density variable approach is proposed for topology optimization of geometrically nonlinear structures as mentioned in this paper.