7 citations · 12 across the 9 of their papers we have counts for
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math.NA2023
Augmented physics informed extreme learning machine to solve the biharmonic equations via Fourier expansions
Xi'an Li, Jinran Wu, Yujia Huang +4
To address the sensitivity of parameters and limited precision for physics-informed extreme learning machines (PIELM) with common activation functions, such as sigmoid, tangent, an…
math.NA2023★ 1 cited
Solving a class of multi-scale elliptic PDEs by means of Fourier-based mixed physics informed neural networks
Xi'an Li, Jinran Wu, You-Gan Wang +2
Deep neural networks have garnered widespread attention due to their simplicity and flexibility in the fields of engineering and scientific calculation. In this study, we probe int…
math.DS2023★ 1 cited
Physical informed neural networks with soft and hard boundary constraints for solving advection-diffusion equations using Fourier expansions
Xi'an Li, Jiaxin Deng, Jinran Wu +3
Deep learning methods have gained considerable interest in the numerical solution of various partial differential equations (PDEs). One particular focus is physics-informed neural…