Cunliang Pan1, Shi Feng2, Shengyang Tao2, Hongwu Zhang1, Yonggang Zheng1,3, Hongfei Ye1,*
The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.1, pp. 1-1, 2024, DOI:10.32604/icces.2024.011132
Abstract Capillarity is prevalent in nature, daily life, and industrial processes, governed by the fundamental Young-Laplace equation. Solving this equation not only enhances our understanding of natural phenomena but also provides valuable insights into industrial advancements. To address challenges posed by conventional numerical methods in parameter identification and complex boundary condition handling, the Young-Laplace Physics-informed Neural Network (Y-L PINN) is introduced to solve the Young-Laplace equation within a tubular domain. Through computational analyses focusing on the classical capillary rise case, the proposed method's accuracy is affirmed through comparisons with Jurin's law, experimental data, and numerical results.… More >
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