13 citations · 54 across the 32 of their papers we have counts for
8 papers · 1 filter
Layer-Parallel Training of Deep Residual Neural Networks
S. Günther, L. Ruthotto, J. B. Schroder +2
Residual neural networks (ResNets) are a promising class of deep neural networks that have shown excellent performance for a number of learning tasks, e.g., image classification an…
Global Aerodynamic Design Optimization via Primal-Dual Aggregation Method
Emre Özkaya, Nicolas R. Gauger
Global aerodynamic design optimization using Euler or Navier-Stokes equations requires very reliable surrogate modeling techniques since the computational effort for the underlying…
Accurate gradient computations for shape optimization via discrete adjoints in CFD-related multiphysics problems
Ole Burghardt, Nicolas R. Gauger
As more and more multiphysics effects are entering the field of CFD simulations, this raises the question how they can be accurately captured in gradient computations for shape opt…
On high-order pressure-robust space discretisations, their advantages for incompressible high Reynolds number generalised Beltrami flows and beyond
Nicolas R. Gauger, Alexander Linke, Philipp W. Schroeder
An improved understanding of the divergence-free constraint for the incompressible Navier--Stokes equations leads to the observation that a semi-norm and corresponding equivalence…
An Approximate Newton Smoothing Method for Shape Optimization
Jonas Kusch, Stephan Schmidt, Nicolas R. Gauger
A novel methodology to efficiently approximate the Hessian for numerical shape optimization is considered. The method enhances operator symbol approximations by including body fitt…
On the Stability of Gradient Based Turbulent Flow Control without Regularization
Emre Özkaya, Nicolas R. Gauger, Daniel Marinc +1
In this paper, we discuss selected adjoint approaches for the turbulent flow control. In particular, we focus on the application of adjoint solvers for the scope of noise reduction…