most citedPhysics-Informed Learning of Aerosol Microphysics

3 citations · 4 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CE20231 cited

Data-driven aerodynamic shape design with distributionally robust optimization approaches

Long Chen, Jan Rottmayer, Lisa Kusch +2

We formulate and solve data-driven aerodynamic shape design problems with distributionally robust optimization (DRO) approaches. Building on the findings of the work \cite{gotoh201…

cs.MS2023

Integrating Enzyme-generated functions into CoDiPack

M. Sagebaum, M. Aehle, N. R. Gauger

In operator overloading algorithmic differentiation, it can be beneficial to create custom derivative functions for some parts of the code base. For manual implementations of the d…

math.OC2023

A gradient descent akin method for constrained optimization: algorithms and applications

Long Chen, Kai-Uwe Bletzinger, Nicolas R. Gauger +1

We present a first-order method for solving constrained optimization problems. The method is derived from our previous work, a modified search direction method inspired by singular…

physics.flu-dyn2023

Trailing-Edge Noise Reduction using Porous Treatment and Surrogate-based Global Optimization

Jan Rottmayer, Emre Özkaya, Sutharsan Satcunanathan +6

Broadband noise reduction is a significant problem in aerospace and industrial applications. Specifically, the noise generated from the trailing edge of an airfoil poses a challeng…

math.NA2022

QFT-based Homogenization

Felix Givois, Matthias Kabel, Nicolas Gauger

Efficient numerical characterization is a key problem in composite material analysis. To follow accuracy improvement in image tomography, memory efficient methods of numerical char…

cs.LG20223 cited

Physics-Informed Learning of Aerosol Microphysics

Paula Harder, Duncan Watson-Parris, Philip Stier +3

Aerosol particles play an important role in the climate system by absorbing and scattering radiation and influencing cloud properties. They are also one of the biggest sources of u…