Marcel Sauer, DLR Cologne, Institute AT Title: Development of an optimization based automatic block structured grid generation method Abstract: In the development in turbomachinery components, flow simulations evaluate the design based on a discrete representation: the grid. The complexity of … Continue reading →

Prof. Nijso Beishuizen, Bosch Deventer and Eindhofen University of Technology Title: Adjoint-based design optimization for combustion applications Abstract: Combustion is used in many devices in the chemical industry, in transport, heating and power. Often, design requirements are competing with each … Continue reading →

Paula Harder, Fraunhofer ITWM, Kaiserslautern Title: Physics-Constrained Learning of Aerosol Microphysics Abstract: 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 … Continue reading →

Tahmineh Zakizadeh Fallahabadi, Aon Solution Germany GmbH, Wiesbaden Title: Hyperparameter Optimization for Machine Learning Models using Bayesian Optimization Abstract: Hyperparameters are important for machine learning algorithms since they directly control the behaviours of training algorithms and have a significant effect … Continue reading →

Vassilios Yfantis, Chair of Machine Tools and Control Systems (WSKL), TU Kaiserslautern Title: Distributed Optimization of Separable Convex and Integer Programs by Quadratically Approximated Dual Ascent Abstract: In this talk, a new algorithm for dual decomposition-based distributed optimization is presented. … Continue reading →

Rozan I. Rosandi, Differential-Algebraic Systems Group, TU Kaiserslautern Title: A Riemannian Framework for the Isogeometric Shape Optimization of Thin Shells Abstract: Structural optimization is concerned with finding an optimal design for a structure under mechanical load. In this talk, we … Continue reading →

Prof. Andrea Walther, Department of Mathematics, Humboldt University Berlin Title: On a semismooth conjugate gradient method Abstract: In machine learning and other large scale applications, nowadays deterministic and stochastic variants of the steepest descent method are widely used for the … Continue reading →

Rohit Pochampalli, Chair for Scientific Computing (SciComp), TU Kaiserslautern Title: Scale Separation in Convolutional Neural Networks: A Multigrid Approach Abstract: The remarkable performance of convolutional neural networks on computer vision tasks is closely linked to the properties of the convolutional … Continue reading →

Dr. Long Chen, Chair for Scientific Computing (SciComp), TU Kaiserslautern Title: A Gradient Descent Akin Method for Constrained Optimization Abstract: Motivated by the applications of large-scale shape optimization problems and inspired by singular value decomposition, we present a “gradient descent … Continue reading →