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With this study we investigate the accuracy of deep learning models for the inference of Reynolds-Averaged Navier-Stokes solutions.
A one-equation turbulence model for aerodynamic flows
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Continuous adjoint approach for the spalart-allmaras model in aerodynamic optimization
Alfonso Bueno-Orovio, Carlos Castro, Francisco Palacios, and Enrique Zuazua · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Rectifier nonlinearities improve neural network acoustic models
Andrew L. Maas, Awni Y. Hannun, and Andrew Y. Ng · 2013
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Tracking deformable objects with point clouds
John Schulman, Alex Lee, Jonathan Ho, and Pieter Abbeel · 2013
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Application of supervised learning to quantify uncertainties in turbulence and combustion modeling
Brendan Tracey, Karthik Duraisamy, and Juan Alonso · 2013
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Bayesian estimates of parameter variability in the k–
WN Edeling, Pasquale Cinnella, Richard P Dwight, and Hester Bijl · 2014
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Generative adversarial nets
Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
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Conditional generative adversarial nets
Mehdi Mirza and Simon Osindero · 2014
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Velocity/pressure-gradient correlations in a FORANS approach to turbulence modeling
Svetlana Poroseva and Scott M Murman · 2014
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Detecting potential falling objects by inferring human action and natural disturbance
Bo Zheng, Yibiao Zhao, C Yu Joey, Katsushi Ikeuchi, and Song-Chun Zhu · 2014
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Segnet: A deep convolutional encoder-decoder architecture for image segmentation
Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla · 2015
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3d reasoning from blocks to stability
Zhaoyin Jia, Andrew C Gallagher, Ashutosh Saxena, and Tsuhan Chen · 2015
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Evaluation of machine learning algorithms for prediction of regions of high reynolds averaged navier stokes uncertainty
Julia Ling and J Templeton · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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A machine learning strategy to assist turbulence model development
Brendan D Tracey, Karthikeyan Duraisamy, and Juan J Alonso · 2015
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Interaction networks for learning about objects, relations and physics
Peter Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, et al · 2016
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A compositional object-based approach to learning physical dynamics
Michael B Chang, Tomer Ullman, Antonio Torralba, and Joshua B Tenenbaum · 2016
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Machine-learning-augmented predictive modeling of turbulent separated flows over airfoils
Anand Pratap Singh, Shivaji Medida, and Karthik Duraisamy · 2017
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Splash modeling with neural networks
Kiwon Um, Xiangyu Hu, and Nils Thuerey · 2017
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Visual interaction networks
Nicholas Watters, Daniel Zoran, Theophane Weber, Peter Battaglia, Razvan Pascanu, and Andrea Tacchetti · 2017
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Long-term forecasting using tensor-train rnns
Rose Yu, Stephan Zheng, Anima Anandkumar, and Yisong Yue · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros · 2017
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Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, and Kilian Q. Weinberger · 2016
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Image-to-image translation with conditional adversarial networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros · 2016
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Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
Julia Ling, Andrew Kurzawski, and Jeremy Templeton · 2016
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Photo-realistic single image super-resolution using a generative adversarial network
Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et al · 2016
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Unsupervised representation learning with deep convolutional generative adversarial networks
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Application of Convolutional Neural Network to Predict Airfoil Lift Coefficient
Y. Zhang, W.-J. Sung, and D. Mavris · 2017
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Deep neural networks for data-driven turbulence models
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