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Hongtao Bi

affiliation not provided to SSRN

  • affiliation not provided to SSRN

SCHOLARLY PAPERS

4

DOWNLOADS

191

TOTAL CITATIONS

0

Scholarly Papers (4)

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1.

Fc-Pinns: Physics-Informed Neural Networks for Solving Neutron Diffusion Eigenvalue Problem with Interface Considerations

Number of pages: 42 Posted: 10 May 2025
Hongtao Bi, meiqi song, Tengfei Zhang and Xiaojing Liu
affiliation not provided to SSRN, affiliation not provided to SSRN, Shanghai Jiao Tong University (SJTU) and Shanghai Jiao Tong University (SJTU)
Downloads 92 (736,406)

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PINNs, eigenvalue, interface, contact boundary, neutron diffusion

2.

Hybrid Symbolic Regression for Data-Driven Discovery: Governing Dimensionless Numbers in Supercritical Heat Transfer

Number of pages: 32 Posted: 27 May 2025
Yunzhi Shi, meiqi song, Hongtao Bi, Wei Xu and Xiaojing Liu
affiliation not provided to SSRN, affiliation not provided to SSRN, affiliation not provided to SSRN, affiliation not provided to SSRN and Shanghai Jiao Tong University (SJTU)
Downloads 44 (1,133,231)

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supercritical fluid, heat transfer, data-drive, neural network, scaling analysis

3.

Accelerating global low-carbon energy transitions with regionally optimized nuclear–renewable microgrids

Number of pages: 31 Posted: 27 Oct 2025
Xinyu Li, Yan Liu, Hongtao Bi and Xiang Chai
affiliation not provided to SSRN, affiliation not provided to SSRN, affiliation not provided to SSRN and Shanghai Jiao Tong University (SJTU)
Downloads 33 (1,277,121)

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Renewable energy, Nuclear, Hybrid Energy System, Multi-objective optimization, GHG；LCOE

4.

Hybrid Symbolic Regression for Data-Driven Discovery:Governing Dimensionless Numbers in Supercritical Heat Transfer

Number of pages: 32 Posted: 10 Jun 2025
Yunzhi Shi, meiqi song, Hongtao Bi, Wei Xu and Xiaojing Liu
affiliation not provided to SSRN, affiliation not provided to SSRN, affiliation not provided to SSRN, affiliation not provided to SSRN and Shanghai Jiao Tong University (SJTU)
Downloads 22 (1,424,331)

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supercritical fluid, heat transfer, data-drive, neural network, scaling analysis