Jiyuan Tu, Guan Heng Yeoh, Chaoqun Liu, and Yao Tao. Computational fluid dynamics: a practical approach. Elsevier, 2023.
Philipp Holl, Vladlen Koltun, Kiwon Um, and Nils Thuerey. phiflow: A Differentiable PDE Solving Framework for Deep Learning via Physical Simulations, 2020. http://montrealrobotics.ca/diffcvgp/assets/papers/3.pdf.
Shadman Sakib, Nazib Ahmed, Ahmed Jawad Kabir, and Hridon Ahmed. An overview of convolutional neural network: Its architecture and applications. 2019.
Yong Yu, Xiaosheng Si, Changhua Hu, and Jianxun Zhang. A review of recurrent neural networks: Lstm cells and network architectures. Neural computation, 31(7):1235–1270, 2019.
Alexander IJ Forrester, Neil W Bressloff, and Andy J Keane. Optimization using surrogate models and partially converged computational fluid dynamics simulations. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 462(2071):2177–2204, 2006.
John A Schaefer, Andrew W Cary, and Manas Khurana. Surrogate model and discretization error impacts on the fluid dynamics uncertainty quantification challenge problem. In AIAA SCITECH 2024 Forum, page 0706, 2024.
Hongwei Tang, Jean Rabault, Alexander Kuhnle, Yan Wang, and Tongguang Wang. Robust active flow control over a range of reynolds numbers using an artificial neural network trained through deep reinforcement learning. Physics of Fluids, 32(5), 2020.
Jean Rabault and Alexander Kuhnle. Accelerating deep reinforcement learning strategies of flow control through a multi-environment approach. Physics of Fluids, 31(9), 2019.
Nils Thuerey, Philipp Holl, Maximilian Mueller, Patrick Schnell, Felix Trost, and Kiwon Um. Physics-based deep learning. arXiv preprint arXiv:2109.05237, 2021.
Saeed Masoudnia and Reza Ebrahimpour. Mixture of experts: a literature survey. Artificial Intelligence Review, 42:275–293, 2014.