研究论文
A hard-constrained physics-informed neural network framework for solving steady incompressible flows
Jinxing Ba, Shuangshuang Fan, Jiayi Liu, Yao Ji, Haiquan Wang
Sun Yat-sen University Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)
来源Ocean Engineering
年份2026
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物理Model Reduction and Neural Networks
Neural Networks and Reservoir Computing · Generative Adversarial Networks and Image Synthesis
参考文献 26
Neural algorithm for solving differential equations
被引 423Hyuk Lee, In Seok Kang · Journal of Computational Physics · 1990
Smoothed Particle Hydrodynamics (SPH): an Overview and Recent Developments
被引 1,833Moubin Liu, G. R. Liu · Archives of Computational Methods in Engineering · 2010
High-Re solutions for incompressible flow using the Navier-Stokes equations and a multigrid method
被引 4,260Urmila Ghia, K. N. Ghia, C. T. Shin · Journal of Computational Physics · 1982
此处列出前 3 条
引用本文 2
State-of-the-art implementations of PINNs for the lid-driven cavity problem – a critical review with future perspectives
被引 0Mohammad Sheikholeslami, Saeed Salehi, Wengang Mao · Results in Engineering · 2026
Physics-Informed Neural Networks Versus Differential Transform Method for Reduced Second-Order ODEs in Membrane Shell Theory
被引 0Rafał Brociek, Mariusz Pleszczyński, Oliwier Wójcik · Symmetry · 2026
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