617 citations · 654 across the 4 of their papers we have counts for
5 papers
SuperWing: a comprehensive transonic wing dataset for data-driven aerodynamic design
Yunjia Yang, Weishao Tang, Mengxin Liu +3
Machine-learning surrogate models have shown promise in accelerating aerodynamic design, yet progress toward generalizable predictors for three-dimensional wings has been limited b…
Exploring Physical Latent Spaces for High-Resolution Flow Restoration
Chloe Paliard, Nils Thuerey, Kiwon Um
We explore training deep neural network models in conjunction with physics simulations via partial differential equations (PDEs), using the simulated degrees of freedom as latent s…
Physics-driven Learning of the Steady Navier-Stokes Equations using Deep Convolutional Neural Networks
Hao Ma, Yuxuan Zhang, Nils Thuerey +2
Recently, physics-driven deep learning methods have shown particular promise for the prediction of physical fields, especially to reduce the dependency on large amounts of pre-comp…
Spot the Difference: Accuracy of Numerical Simulations via the Human Visual System
Kiwon Um, Xiangyu Hu, Bing Wang +1
Comparative evaluation lies at the heart of science, and determining the accuracy of a computational method is crucial for evaluating its potential as well as for guiding future ef…
Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations of Airfoil Flows
Nils Thuerey, Konstantin Weissenow, Lukas Prantl +1
With this study we investigate the accuracy of deep learning models for the inference of Reynolds-Averaged Navier-Stokes solutions. We focus on a modernized U-net architecture, and…