Events

Here you can find upcoming and past events organized by our group.

  • Mon
    25
    Apr
    2022
    Fri
    29
    Jul
    2022

    Prof. Dr. Nicolas Gauger, Chair for Scientific Computing (SciComp), TU Kaiserslautern

    SciComp Seminar Series

    Please contact Prof. Gauger, if you want to register for an online talk in our SciComp Seminar Series or just to register for the seminar.

    A list of the already scheduled talks can be found –> here:

  • Thu
    23
    Jun
    2022

    12:00Hybrid (Room 32-349 and via Zoom)

    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 minimization of objectives that are only piecewise smooth. As alternative, in this talk we present a deterministic descent method based on the generalization of rescaled conjugate gradients proposed by Phil Wolfe in 1975 for objectives that are convex. Without this assumption the new method exploits semismoothness to obtain conjugate pairs of generalized gradients such that it can only converge to Clarke stationary points. In addition to the theoretical analysis, we present preliminary numerical results.

    How to join online

    The talk is held online via Zoom. You can join with the following link:
    https://uni-kl-de.zoom.us/j/62521592603?pwd=VktnbVlrWHhiVmxQTzNWQlkxSy9WZz09

  • Thu
    07
    Jul
    2022

    12:00Hybrid (Room 32-349 and via Zoom)

    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 on the performance of machine learning models. Bayesian optimization is an optimization framework for the global optimization of expensive Blackbox functions, which recently gained traction in Hyperparameter optimization for machine learning algorithms. In this talk, Bayesian optimization as a method to optimize hyperparameter in Machine Learning models is reviewed. First, we will consider the traditional Bayesian optimization method, then we will consider new suggested methods, which are based on Bayesian Optimization and supposed to be more efficient than the traditional Bayesian optimization method.

    How to join online

    The talk is held online via Zoom. You can join with the following link:
    https://uni-kl-de.zoom.us/j/62521592603?pwd=VktnbVlrWHhiVmxQTzNWQlkxSy9WZz09

  • Thu
    14
    Jul
    2022

    12:00Hybrid (Room 32-349 and via Zoom)

    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 consider thin elastic shell structures based on the linearized Koiter model, whose shape can be described by a surface embedded in Euclidean space. We regard the set of all embeddings of the surface as an infinite-dimensional Riemannian manifold and perform optimization in this setting using the Riemannian shape gradient. Non-uniform rational B-splines (NURBS) are employed to parameterize the surface and solve the underlying equations that govern the mechanical behavior of the shell via isogeometric analysis (IGA). By representing NURBS patches as B-spline patches in projective space, NURBS weights can also be incorporated into the optimization routine. We discuss the practical implementation of the method and demonstrate our approach on the compliance minimization of a half-cylindrical shell under static load and fixed area constraint.

    How to join online

    The talk is held online via Zoom. You can join with the following link:
    https://uni-kl-de.zoom.us/j/62521592603?pwd=VktnbVlrWHhiVmxQTzNWQlkxSy9WZz09