Bekim Berisha: Catalogue data in Spring Semester 2023 |
| Name | Dr. Bekim Berisha |
| Address | Institut für virtuelle Produktion ETH Zürich, PFA G 17 Technoparkstrasse 1 8005 Zürich SWITZERLAND |
| Telephone | +41 44 632 78 46 |
| berisha@ivp.mavt.ethz.ch | |
| URL | https://mohr.ethz.ch/ |
| Department | Mechanical and Process Engineering |
| Relationship | Lecturer |
| Number | Title | ECTS | Hours | Lecturers | |
|---|---|---|---|---|---|
| 151-0042-01L | Engineering Tool: FEM-Programs  The Engineering Tools courses are for MAVT Bachelor’s degree students only. | 0.4 credits | 1K | B. Berisha | |
| Abstract | The course "Introduction to FEM programs" familiarizes the students with performing of simple structural analyses with the finite-element method. | ||||
| Learning objective | Becoming familiar with using a modern finite-element program. Learn how to perform structural analyses of complex parts designed with CAD. Critical results interpretation by way of convergence analysis. | ||||
| Content | - FEM-Theorie - Charakterisierung der FEM - Grundlagen der Elastizitätstheorie - Randwertproblem in der Verschiebungsformulierung - Standardformulierung/Variationsprinzip - Elementtypen - Randbedingungen - Strukturanalyse mit FEM - Nichtlinearitäten (iterative/inkrementelle Lösungssuche) - Dynamische Prozesse | ||||
| Lecture notes | Course material: The material is based on the course in spring semester 2019 and are complemented according to our needs. | ||||
| Literature | No textbooks required | ||||
| Prerequisites / Notice | Installation von ABAQUS 2021 - Teaching Für den Toolkurs wird "Abaqus 2021 -Teaching" benötigt. Die Installationsdatei, sowie die Installationsanleitung, sind auf dem IT-SHOP zu finden (https://itshop.ethz.ch/EndUser/Items/Home). Abaqus 2021 - Teaching ist NUR für WINDOWS und LINUX verfügbar. Es stehen keine Rechner zur Verfügung! Für weitere Informationen siehe "Ankündigungen" in MOODLE | ||||
| 151-0840-00L | Optimization and Machine Learning | 4 credits | 2V + 2U | B. Berisha, D. Mohr | |
| Abstract | The course teaches the basics of nonlinear optimization and concepts of machine learning. An introduction to the finite element method allows an extension of the application area to real engineering problems such as structural optimization and modeling of material behavior on different length scales. | ||||
| Learning objective | Students will learn mathematical optimization methods including gradient based and gradient free methods as well as established algorithms in the context of machine learning to solve real engineering problems, which are generally non-linear in nature. Strategies to ensure efficient training of machine learning models based on large data sets define another teaching goal of the course. Optimization tools (MATLAB, LS-Opt, Python) and the finite element program ABAQUS are presented to solve both general and real engineering problems. | ||||
| Content | - Introduction into Nonlinear Optimization - Design of Experiments DoE - Introduction into Nonlinear Finite Element Analysis - Optimization based on Meta Modeling Techniques - Shape and Topology Optimization - Robustness and Sensitivity Analysis - Fundamentals of Machine Learning - Generalized methods for regression and classification, Neural Networks, Support Vector machines - Supervised and unsupervised learning | ||||
| Lecture notes | Lecture slides and literature | ||||