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一个基于数据的解决方案框架和使用条件生成对抗网络的PDE参数估计

Teeratorn Kadeethum1, Daniel O'Malley2, Jan Niklas Fuhg1

  • 1Sibley School of Mechanical and Aerospace, EngineeringCornell University, Ithaca, NY, USA.

Nature computational science
|January 13, 2024
PubMed
概括

本研究引入了一种新的深度学习框架,使用条件生成对抗网络 (cGAN) 来解决多孔介质的复杂部分微分方程 (PDE). 该方法显著加快了模拟,并提高了前向和反向建模任务的准确性.

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