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Chinese Physics C > 2025, Vol. 49 > Issue(8): 084109 DOI: 10.1088/1674-1137/add9fc

Application of physics-informed neural networks to multidimensional quantum tunneling

  • 1.

    China Institute of Atomic Energy, Beijing 102413, China

  • 2.

    College of Physics and Technology, Guangxi Normal University, Guilin 541004, China

  • Physics-Informed Neural Networks (PINNs) have emerged as a powerful tool for solving high-dimensional partial differential equations and have demonstrated promising results across various fields of physics and engineering. In this paper, we present the first application of PINNs to quantum tunneling in heavy-ion fusion reactions. By incorporating the physical laws directly into the neural network's loss function, PINNs enable the accurate solution of the multidimensional Schrödinger equation, whose wavefunction has substantial oscillations. The calculated quantum tunneling probabilities exhibit good agreement with those obtained using the finite element method at the considered near barrier energy region. Furthermore, we demonstrate a significant advantage of the PINN approach to save and fine-tune pre-trained neural networks for related tunneling calculations, thereby enhancing computational efficiency and adaptability.
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P. W. Wen, C. J. Lin, L. Yang, H. M. Jia and N. R. Ma. Application of physics-informed neural network on multidimensional quantum tunneling[J]. Chinese Physics C. doi: 10.1088/1674-1137/add9fc
P. W. Wen, C. J. Lin, L. Yang, H. M. Jia and N. R. Ma. Application of physics-informed neural network on multidimensional quantum tunneling[J]. Chinese Physics C. doi:10.1088/1674-1137/add9fc shu
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Received: 2025-03-16
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