PHYSICS-INFORMED NEURAL NETWORK FOR SOLVING HAUSDORFF DERIVATIVE POISSON EQUATIONS

Guozheng Wu, Fajie Wang and Lin Qiu
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Guozheng Wu: College of Mechanical and Electrical Engineering, National Engineering Research Center for Intelligent Electrical, Vehicle Power System, Qingdao University, Qingdao 266071, P. R. China
Fajie Wang: College of Mechanical and Electrical Engineering, National Engineering Research Center for Intelligent Electrical, Vehicle Power System, Qingdao University, Qingdao 266071, P. R. China
Lin Qiu: College of Mechanical and Electrical Engineering, National Engineering Research Center for Intelligent Electrical, Vehicle Power System, Qingdao University, Qingdao 266071, P. R. China

FRACTALS (fractals), 2023, vol. 31, issue 06, 1-15

Abstract: This paper proposed a new physics-informed neural network (PINN) for solving the Hausdorff derivative Poisson equations (HDPEs) on irregular domains by using the concept of Hausdorff fractal derivative. The present scheme transforms the numerical solution of partial differential equation into an optimization problem including governing equation and boundary conditions. Like the meshless method, the developed PINN does not require grid generation and numerical integration. Moreover, it can freely address irregular domains and non-uniformly distributed nodes. The present study investigated different activation functions, and given an optimal choice in solving the HDPEs. Compared to other existing approaches, the PINN is simple, straightforward, and easy-to-program. Numerical experiments indicate that the new methodology is accurate and effective in solving the HDPEs on arbitrary domains, which provides a new idea for solving fractal differential equations.

Keywords: Physics-Informed Neural Network; Hausdorff Derivative; Poisson Equations; Activation Function; Machine Learning (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0218348X23401035

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