Normalized Fourier induced coupled PINNs to solve the Dirichlet biharmonic equations in a large-scale domain

Yujia HUANG , Jinran WU , Zhe DING , Xi’an LI

Front. Comput. Sci. ›› 2026, Vol. 20 ›› Issue (7) : 2007354

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Front. Comput. Sci. ›› 2026, Vol. 20 ›› Issue (7) : 2007354 DOI: 10.1007/s11704-025-50366-4
Artificial Intelligence
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Normalized Fourier induced coupled PINNs to solve the Dirichlet biharmonic equations in a large-scale domain

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Yujia HUANG, Jinran WU, Zhe DING, Xi’an LI. Normalized Fourier induced coupled PINNs to solve the Dirichlet biharmonic equations in a large-scale domain. Front. Comput. Sci., 2026, 20(7): 2007354 DOI:10.1007/s11704-025-50366-4

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References

[1]

Raissi M, Perdikaris P, Karniadakis G E . Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 2019, 378: 686–707

[2]

Sirignano J, Spiliopoulos K . DGM: a deep learning algorithm for solving partial differential equations. Journal of Computational Physics, 2018, 375: 1339–1364

[3]

Lyu L, Zhang Z, Chen M, Chen J . MIM: a deep mixed residual method for solving high-order partial differential equations. Journal of Computational Physics, 2022, 452: 110930

[4]

Huang Y, Li X, Wu J. Fourier heuristic PINNs to solve the biharmonic equations based on its coupled scheme. Available at SSRN: dx.doi.org/10.2139/ssrn.5014416

[5]

Lin G, Hu P, Chen F, Chen X, Chen J, Wang J, Shi Z . BINet: learn to solve partial differential equations with boundary integral networks. CSIAM Transactions on Applied Mathematics, 2023, 4( 2): 275–305

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