Zuqi Tang

Associate Professor

Université de Lille

France

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About

Zuqi Tang is an Associate Professor at Université de Lille in France. His research interests focus on model order reduction techniques, particularly in the context of nonlinear parametric electromagnetic problems and digital twins. He has published extensively on topics including tensor decomposition, physics-informed neural networks, and modeling the magnetic properties of electrical steels.

Articles (21)

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2025

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Collaborators (2)

Abdelkader Benabou

Associate Professor

Université de Lille

FRANCE

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Yvonnick Le Menach

Full professor

Université de Lille

FRANCE

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Zuqi Tang

Research Interests

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About

Articles (21)

Tensor decomposition-based DEIM for model order reduction applied to nonlinear parametric electromagnetic problems

Tensor Decomposition‐Based Model Order Reduction Applied to Multi‐Parameter Electromagnetic Problems in the Context of Digital Twins

Physics‐Informed Neural Network for Magnetization Distribution Estimation

Real–Virtual Sensor Parameterized-Background Data-Weak Method for Digital Twin State Estimation

Towards Purely Parameter-Specific Model Order Reduction: A Tensor Decomposition-Based Approach

ODF-based model considering compressive stress for modeling the magnetic properties of Grain-oriented electrical steels

Goal-oriented error estimation based on equilibrated flux reconstruction for the approximation of the harmonic formulations in eddy current problems

Consideration of tensile stress in the ODF-based approach for modelling the first magnetization curves of Grain-Oriented Electrical Steels

Optimal Selection of Angular Input Data for the ODF-Based Model Applied to B–H Magnetization Curves of Grain-Oriented Electrical Steels

Study of the ability of MBN based NDT to distinguish high-performance martensitic steel grades

Electromagnetic Modeling of PCB Based on Darwin's Model Combined With Degenerated Prism Whitney Elements

Goal-oriented error estimation based on equilibrated flux and potential reconstruction for the approximation of elliptic and parabolic problems

Stabilized Gauged Formulation of Darwin Model for FEM Computation of Industrial Applications

Further investigation of convolutional neural networks applied in computational electromagnetism under physics‐informed consideration

Improvement of CNN-Based Anisotropic Magnetostatic Field Computation via Adaptive Data Subset Selection

Optimal Sensor Placement via Parameterized Background Data Weak Method applied to Magnetostatic Problems including Anisotropy

Stabilized Gauged Formulation of Darwin Model for FEM Computation of Industrial Applications

Guaranteed Quantity of Interest Error Estimate Based on Equilibrated Flux Reconstruction

Numerical Simulation-Based Investigation of the Limits of Different Quasistatic Models

Fast Time-Domain Solution of Dynamic Electromagnetic Problems Based on Sinc Interpolation

Fast Time-Domain Solution of Dynamic Electromagnetic Problems Based on Sinc Interpolation

Collaborators (2)

Abdelkader Benabou

Yvonnick Le Menach

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