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Deep neural network method to predict the dynamical system response under random excitation of combined Gaussian and Poisson white noises

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  • Jia, Wantao
  • Feng, Xiaotong
  • Hao, Mengli
  • Ma, Shichao

Abstract

Nonlinear dynamical systems excited by combined Gaussian and Poisson white noises are widely found in the scientific field. However, the introduction of Poisson white noise results in the associated forward Kolmogorov equation becoming an integro-differential equation (IDE), which makes it difficult to solve the response of these dynamical systems. At present, traditional numerical IDE solvers are limited due to both the mesh generation and high computational costs. In this context, this paper integrates the multi-element Gauss–Legendre (GL) quadrature into the physics-informed neural networks (PINNs) algorithm to predict the response of systems excited by combined Gaussian and Poisson white noises. A residual-based adaptive distribution (RAD) sampling method, which replaces Latin hypercube sampling (LHS) method, is implemented to adjust the residual points to improve the accuracy and training efficiency of the proposed algorithm. The results demonstrate that the integration of the PINNs algorithm with RAD sampling method dramatically enhances the tolerance of noisy data and expedites the convergence of the PINNs algorithm compared with the LHS sampling method. It has the ability to achieve better performance in a shorter training time and reduces the demand for data during the training process. Additionally, the Monte Carlo (MC) simulation is carried out to demonstrate the effectiveness of the numerical results in this paper.

Suggested Citation

  • Jia, Wantao & Feng, Xiaotong & Hao, Mengli & Ma, Shichao, 2024. "Deep neural network method to predict the dynamical system response under random excitation of combined Gaussian and Poisson white noises," Chaos, Solitons & Fractals, Elsevier, vol. 185(C).
    • Handle: RePEc:eee:chsofr:v:185:y:2024:i:c:s0960077924006866
    DOI: 10.1016/j.chaos.2024.115134
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    References listed on IDEAS

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    1. Ma, Zhiying & Hou, Jie & Zhu, Wenhao & Peng, Yaxin & Li, Ying, 2023. "PMNN: Physical model-driven neural network for solving time-fractional differential equations," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
    2. Shields, Michael D. & Zhang, Jiaxin, 2016. "The generalization of Latin hypercube sampling," Reliability Engineering and System Safety, Elsevier, vol. 148(C), pages 96-108.
    3. Feng, Guolin & Gao, Xinquan & Dong, Wenjie & Li, Jianping, 2008. "Time-dependent solutions of the Fokker–Planck equation of maximally reduced air–sea coupling climate model," Chaos, Solitons & Fractals, Elsevier, vol. 37(2), pages 487-495.
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