3/10/2024 0 Comments Airfoil database b707a![]() A gentle introduction to graph neural networks. SANCHEZ-LENGELING, B., REIF, E., PEARCE, A., and WILTSCHKO, A. Combining differentiable PDE solvers and graph neural networks for fluid flow prediction. Learning mesh-based simulation with graph networks. PFAFF, T., FORTUNATO, M., SANCHEZ-GONZALEZ, A., and BATTAGLIA, P. Medical Image Computing and Computer-Assisted Intervention MICCAI-2015, Springer, Berlin, 234–241 (2015) U-net: convolutional networks for biomedical image segmentation. RONNEBERGER, O., FISCHER, P., and BROX, T. ![]() Proceedings of the 27 th International Conference on Neural Information Processing Systems, MIT Press, Cambridge, 2672–2680 (2014) GOODFELLOW, I., POUGET-ABADIE, J., MIRZA, M., XU, B., WARDE-FARLEY, D., OZAIR, S., COURVILLE, A., and BENGIO, Y. Deep learning methods for Reynolds-averaged Navier-Stokes simulations of airfoil flows. THUEREY, N., WEIßENOW, K., PRANTL, L., and HU, X. Prediction of aerodynamic flow fields using convolutional neural networks. Proceedings of the 22 nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, San Francisco, 481–490 (2016)īHATNAGAR, S., AFSHAR, Y., PAN, S., DURAISAMY, K., and KAUSHIK, S. Convolutional neural networks for steady flow approximation. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 447(2213), 20170844 (2018) Datadriven forecasting of high-dimensional chaotic systems with long short-term memory networks. Data-assisted reduced-order modeling of extreme events in complex dynamical systems. Y., VLACHAS, P., KOUMOUTSAKOS, P., and SAPSIS, T. Unsteady aerodynamic reduced-order modeling based on machine learning across multiple airfoils. Fast pressure distribution prediction of airfoils using deep learning. ![]() HUI, X., BAI, J., WANG, H., and ZHANG, Y. A deep learning approach for efficiently and accurately evaluating the flow field of supercritical airfoils. WU, H., LIU, X., AN, W., CHEN, S., and LYU, H. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 20(1), 30–42 (2012) Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Association for Computational Linguistics, Minneapolis, 4171–4186 (2019)ĭAHL, G. BERT: pre-training of deep bidirectional transformers for language understanding. ImageNet classification with deep convolutional neural networks. KRIZHEVSKY, A., SUTSKEVER, I., and HINTON, G. There is no doubt that the unprecedented speed and accuracy in forecasting steady airfoil flows have massive benefits for airfoil design and optimization. Furthermore, the property of zero-shot super-resolution enables the proposed approach to overcome the limitations of predicting airfoil flows with Cartesian grids, thereby improving the accuracy in the near-wall region. The predictions for flows around airfoils and ellipses demonstrate the superior accuracy and impressive speed of this novel approach. The proposed approach runs several orders of magnitude faster than the traditional numerical methods. Theoretical reasons and experimental results are provided to support the necessity and effectiveness of the improvements made to the FNO, which involve using an additional branch neural operator to approximate the contribution of boundary conditions to steady solutions. An efficient data-driven approach for predicting steady airfoil flows is proposed based on the Fourier neural operator (FNO), which is a new framework of neural networks.
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