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Yang Hu

Beijing Jiaotong University

No.3 of Shangyuan Residence Haidian District

Beijing, 100089

China

SCHOLARLY PAPERS

5

DOWNLOADS

241

TOTAL CITATIONS

0

Scholarly Papers (5)

1.

Boundary Region Reinforcement Physics-Informed Neural Networks for Solving Partial Differential Equations

Number of pages: 23 Posted: 30 Oct 2024
Johns Hopkins University, Tsinghua University, Beijing Jiaotong University and Tsinghua University
Downloads 106 (674,591)

Abstract:

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Partial differential equations, Physics-informed neural networks, Boundary Region Reinforcement, Computational accuracy, Deep learning, Thermo-elastic coupling system

2.

A Modified Diffuse Domain-Lattice Boltzmann Model for Heat Transfer Problems in Complex Geometries

Number of pages: 18 Posted: 24 Dec 2021
Beijing Jiaotong University, Xiangtan University, Tsinghua University and Tsinghua University - State Key Laboratory of Tribology
Downloads 59 (980,173)

Abstract:

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Diffuse domain method, Lattice Boltzmann Method, Regularized method, Heat Transfer, complex geometries, Level set method

3.

Design and Experimental Study of a Large Diameter Convergent Staggered Pole Tooth Ferromagnetic Fluid Sealing Device

Number of pages: 20 Posted: 14 Aug 2024
Guangxi University of Science and Technology, Guangxi University of Science and Technology, Guangxi University of Science and Technology, University of Science and Technology Beijing and Beijing Jiaotong University
Downloads 36 (1,236,159)

Abstract:

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Large diameter, Staggered pole tooth, Ferromagnetic fluid sealing, Comparative experimental study

4.

Algebraic Loss Augmentation: Steering Conflicting Residuals for Coupled Physics-Informed Neural Networks

Number of pages: 25 Posted: 13 Apr 2026
affiliation not provided to SSRN, Tsinghua University, affiliation not provided to SSRN, affiliation not provided to SSRN, Beijing Jiaotong University, Tsinghua University and Independent
Downloads 25 (1,470,786)

Abstract:

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Physics-Informed Neural Networks (PINNs), Gradient Conflict, Algebraic Loss Augmentation (ALA), high-fidelity solutions

5.

On the Diminishing Returns of Higher-Order Gradient Constraints in Gradient-enhanced physics-informed neural networks: Order Selection and Directional Anisotropy

Number of pages: 19 Posted: 14 Feb 2026
affiliation not provided to SSRN, affiliation not provided to SSRN, Tsinghua University, affiliation not provided to SSRN, Beijing Jiaotong University, Independent and affiliation not provided to SSRN
Downloads 15 (1,500,778)

Abstract:

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Gradient-enhanced physics-informed neural networks, Higher-order derivative constraints, Hard constraints, Boundary element method