34 citations · 41 across the 9 of their papers we have counts for
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Evaluation of State-of-the-Art Deep Learning Architectures for Aerodynamical Predictions
Jan Scherz, Derrick Hines, Philipp Bekemeyer
Surrogate models are used to substitute classical numerical solvers in engineering applications where the computational cost of the latter becomes infeasible. For instance, in aero…
Physics-Informed Neural Networks for Transonic Flows around an Airfoil
Simon Wassing, Stefan Langer, Philipp Bekemeyer
Physics-informed neural networks have gained popularity as a deep-learning based parametric partial differential equation solver. Especially for engineering applications, this appr…
Physics-Informed Neural Networks for Parametric Compressible Euler Equations
Simon Wassing, Stefan Langer, Philipp Bekemeyer
The numerical approximation of solutions to the compressible Euler and Navier-Stokes equations is a crucial but challenging task with relevance in various fields of science and eng…