# Talks

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Talks 11 to 20 of 489 | show all

Date | Time | Venue | Talk |
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05/05/21 | 03:00 pm | BBB |
Training of YOLO with altered activation function [Bachelorarbeitsvortrag]Minh An Pham |

05/03/21 | 03:00 pm | Zoom |
Hypothesis tests in regression models with long-range dependenceMatthias Lienau, Institute of Mathematics, Chair of StochasticsIn my inaugural talk I would like to introduce myself and present the topic of my master thesis. To this end, I will first provide a brief introduction to empirical processes and long-range dependence. Afterwards, we consider the problem of testing the equality of two non-parametric regression functions. Finally, we provide a goodness of fit test for the error distribution. |

04/26/21 | 03:00 pm | Zoom |
Inertial Particles in a viscous fluid: The Maxey-Riley equation.Julio Urizarna The characterisation of the dynamics of a small inertial particle in a viscous fluid is a problem that dates to Stokes[1], back in 1851. Since his first attempt, many have tried and several formulas have been obtained for different types of flows, as well as more general cases; however, the scientific community did not agree in a general formula until 1983, when M. Maxey and J. Riley[2] obtained a formula from first principles. This formula includes an integro-differential term, called the Basset History term, which requires information for the whole history of the particle dynamics and creates difficulties in the numerical implementation due to fast increasing storage requeriments. In the last decade, the Maxey-Riley formula has drawn the interest of many mathematicians and so, local and global existence and uniqueness of mild solutions have been proved ([3] & [4]). Nevertheless, a method to bypass the history term and obtain the trajectory of the particle remained unknown until the publication of an accurate solution method by S.Ganga Prasath et al (2019) [5]. In this presentation I will analyse the Maxey Riley equation and will identify the core ideas within S. Ganga Prasath's method to solve the Maxey Riley equation as well as its implementation for certain fluid flows. [1] Stokes, G. G. (1851). On the effect of the internal friction of fluids on the motion of pendulums. [2] Maxey, M. R., & Riley, J. J. (1983). Equation of motion for a small rigid sphere in a nonuniform flow. The Physics of Fluids, 26(4), 883-889. [3] Farazmand, M., & Haller, G. (2015). The Maxey–Riley equation: Existence, uniqueness and regularity of solutions. Nonlinear Analysis: Real World Applications, 22, 98-106. [4] Langlois, G. P., Farazmand, M., & Haller, G. (2015). Asymptotic dynamics of inertial particles with memory. Journal of nonlinear science, 25(6), 1225-1255. [5] Prasath, S. G., Vasan, V., & Govindarajan, R. (2019). Accurate solution method for the Maxey–Riley equation, and the effects of Basset history. Journal of Fluid Mechanics, 868, 428-460. |

04/12/21 | 03:00 pm | Zoom |
Malliavin calculus and Malliavin-Stein methodVanessa Trapp In this talk, I would like to introduce myself and the topic of my master thesis "Malliavin calculus and Malliavin-Stein method". As indicated by its name, this talk provides a basic overview of the Malliavin calculus and its operators in the case where the underlying process is an isonormal Gaussian process. After this introduction, it is shown how the Malliavin calculus can be combined with Stein's method for the purpose of one-dimensional normal approximation and, particularly, for the derivation of generalized central limit theorems. |

03/30/21 | 01:00 pm | BBB |
Banachs Hyperebenen-Problem (Bachelorarbeitsvortrag)Max Levermann |

03/16/21 | 04:00 pm | Online |
Einfluss von Batch-Normalisierung für verschiedene Aktivierungsfunktionen [Bachelorarbeitsvortrag]Moritz Seefeldt |

03/16/21 | 03:00 pm | Online |
Relation between Activation Function and Weight Initialization in Neural Networks [Bachelorarbeitsvortrag]Erich Doclaf |

03/15/21 | 03:00 pm | Zoom meeting |
A semi-implicit meshfree/particle scheme for the shallow water equations* Dr. Adeleke Bankole, Institute of Mathematics, Hamburg University This presentation introduces the semi-implicit Smoothed Particle Hydrodynamics (SPH) scheme [1] for the shallow water equations following the semi-implicit finite volume and finite difference approach of Casulli [2]. In standard explicit numerical methods, there is often a severe limitation on the time step due to the stability restriction imposed by the CFL condition. To this effect, a semi-implicit SPH scheme is derived, which leads to an unconditionally stable method. The discrete momentum equation is substituted into the discrete continuity equation to obtain a symmetric positive definite linear system for the free surface elevation. The resulting system can be easily solved by a matrix-free conjugate gradient method. Once the new free surface location is known, the velocity at the new time level can be directly computed and the particle positions can subsequently be updated. We further discuss a nonlinear algorithm for treating wetting/drying problems. We derive a mildly nonlinear system for the discrete free surface elevation from the shallow water equations by taking into consideration a correct mass balance in wet regions and in transition regions, i.e. the regions from wet particles to dry particles and those from dry particles to wet particles. The scheme is validated on a two dimensional inviscid hydrostatic free surface flows for the two dimensional shallow water equations and wetting/drying test problem. References [1] A.O. Bankole, A. Iske, T. Rung, M. Dumbser, A meshfree semi-implicit Smoothed Particle Hydrodynamics method for free surface flow. Meshfree Methods for Partial Differential Equations VIII, M. Griebel and M.A. Schweitzer (eds.), Springer LNCSE, Vol. 115, pp. 35-52 (2017). [2] V. Casulli, Semi-Implicit Finite Difference Methods for the Two-Dimensional Shallow Water Equations. Jour. of Comp. Phys., Vol 86. pp. 56-74 (1990). |

02/25/21 | 09:00 am | https://meeting.rz.tuhh.de/b/mar-fza-znh-37y , Code: 785176 |
Mündliche Prüfung zur Dissertation: Fractional Powers of Linear Operators in Locally Convex Vector SpacesJan Meichsner |

02/24/21 | 02:00 pm | Online |
Neuronale Netzwerke mit (approximativ) orthonormalen Gewichtsmatrizen [Bachelorarbeitsvortrag]Marco Zabel |

* Talk within the Colloquium on Applied Mathematics