Module details

M1100-CMS61  Computational Engineering Basics

Module Owner: N.N.
Displayed in timetable as: CMS-CE-EL1
Duration: 4
Number of electives: 0
Credits: 10,0
Start Semester: SuSe 2019
Lecturer Responsible Prof. Dr. Michael Beitelschmidt
michael.beitelschmidt@tu-dresden.de
Qualification Goals The students master the basic skills for the application of computer-aided modelling and simulation in the engineering sciences. You have in-depth knowledge of the application domain and are able to present simulation results intuitively. The students know concrete constructive questions and know the basics of the application discipline as well as its technical vocabulary.
Content The contents of the module can be chosen according to the focus of the student: the basics of mechanics, the basics of automation, computer science methods for graphical representation and control of simulations as well as flow simulation.
Forms of Teaching and Learning The module includes lectures, exercises, seminars, tutorials, internships and project work in the amount of 8 SWS and self-study. The courses must be selected from the CMS-CE-EL1 catalogue to the specified extent; this will be announced at the beginning of the semester, as is customary at the Faculty of Computer Science, including the language of the course, the examination achievements required in each case and the weights of the grades.
Prerequisites for Participation Knowledge in sequential computer programming, algorithms and data structures, analysis of functions of one and several variables, linear algebra (vector and matrix calculation), as well as probability calculation and Statistics at the Bachelor's level is required.

With the following literature, students can prepare for the module:
Harel: Algorithmics - The Spirit of Computing, Addison-Wesley, 2004
Schildt: C ++ from the ground up, McGraw-Hill, 2003
Abelson, Hal; Sussman, Gerald Jay: Structure and Interpretation of Computer Programs. MIT Press, 1985;
Cormen, Leiserson, Rivest & Stein: Introduction to Algorithms, 2nd Edition, MIT Press 2001;
Lax, Terrell: Multivariable Calculus with Applications (Undergraduate Texts in Mathematics), Springer, 2018
Hefferon, Jim: Linear Algebra, http://joshua.smcvt.edu/linearalgebra/, 2008.
Applicability The module is a compulsory module for students of the Computational Engineering track in the Master's Programme Computational Modelling and Simulation.
Prerequisites for the Assignment of Credit Points The credit points are awarded if the module examination is passed. The module examination consists of the examination services specified in the CMS-CE-EL1 catalogue.
Credit Points and Grades The module allows one to earn 10 credit points. The module grade is calculated from the weighted average of the grades of the examination performances according to the CMS-CE-EL1 catalogue.
Frequency of Offer The module is offered each academic year, starting in the summer semester.
Workload The workload is a total of 300 hours.
Duration of Module The module takes two semesters to complete.
Module Number Module Handbook TU Dresden CMS-CE-EL1

Registration periods

Phase Block Register from | to End cancellation
Ohne Auswahlverfahren Vorlesungszeit 04.04.2019 00:00 | 20.01.2020 00:00 30.01.2020 00:00

Courses
Number Name Semester  
DK1100-MA001X Dummykurs CMS 1 SWS  
DK1100-MA002X Dummykurs CMS 2 SWS  
DK1100-MA003X Dummykurs CMS 3 SWS  
DK1100-MA004X Dummykurs CMS 4 SWS  
DK1100-MA004 SCPROG Scientific Programming - Advanced Aspects WiSe 2019/20
DK1100-MA004X Dummykurs CMS 4 SWS SuSe 2019
K1301-1H1625P Multi Body Systems (P)  
K1301-1H1625P Multi Body Systems SuSe 2019
K1100-EX0630V Foundations for Machine Learning (V) 1  
K1107-MA0006V Particle Methods (V) 1  
K1107-MA0006V Particle Methods (L) SuSe 2019
K1107-MA0006V Particle Methods (L) SuSe 2020
K1107-MA0006Ü Particle Methods (Ü) 1  
K1107-MA0006Ü Particle Methods (E) SuSe 2019
K1107-MA0006Ü Particle Methods (E) SuSe 2020
K1107-MA0060V Machine Learning 1 (V) 1  
K1107-MA0060V Foundations for Machine Learning (L) SuSe 2019
K1107-MA0060V Machine Learning 1 Compact event (L) WiSe 2019/20
K1107-MA0060Ü Machine Learning 1 (Ü) 1  
K1107-MA0060Ü Machine Learning 1 (E) WiSe 2019/20
K1208-500130Ü Characterization of micro- and nanoelectronic devices (E) 1  
K1208-500180V Modeling of Nanoelectronic Devices (L) 1  
K1210-1I0150V Antennen (V) 1  
K1210-1I0150V Antennas (L) SuSe 2019
K1210-1I0150V Antennas (L) SuSe 2020
K1210-1I0150Ü Antennen (Ü) 1  
K1210-1I0150Ü Antennas (E) SuSe 2019
K1210-1I0150Ü Antennas (E) SuSe 2020
K1210-1I0151V Wellenausbreitung (V) 1  
K1210-1I0151V Propagation (L) SuSe 2019
K1210-1I0151V Propagation (L) SuSe 2020
K1210-1I0151Ü Wellenausbreitung (Ü) 1  
K1210-1I0151Ü Propagation (E) SuSe 2019
K1210-1I0151Ü Propagation (E) SuSe 2020
K1301-1H0500V System Dynamics (L) 1  
K1301-1H0500V System Dynamics (L) SuSe 2019
K1301-1H0500V System Dynamics (L) SuSe 2020
K1301-1H0500Ü System Dynamics (E) 1  
K1301-1H0500Ü System Dynamics (E) SuSe 2019
K1301-1H0500Ü System Dynamics (E) SuSe 2020
K1302-1H0521P Turbulente Strömungen und deren Modellierung (P) 1  
K1302-1H0521V Turbulente Strömungen und deren Modellierung (V) 1  
K1302-1H0521V Turbulent flows and their modelling (L) SuSe 2020
K1302-1H0521Ü Turbulente Strömungen und deren Modellierung (Ü) 1  
K1302-1H0521Ü Turbulent flows and their modelling (E) SuSe 2020
K1302-1H1630P Numerische Modellierung von Mehrphasenströmungen (P) 1  
K1302-1H1630P Numerical modelling of multiphase flows (P) SuSe 2019
K1302-1H1630P Numerical modelling of multiphase flows (P) SuSe 2020
K1302-1H1630V Numerische Modellierung von Mehrphasenströmungen (V) 1  
K1302-1H1630V Numerical modelling of multiphase flows (L) SuSe 2019
K1302-1H1630V Numerical modelling of multiphase flows (L) SuSe 2020
K1302-1H1630Ü Numerische Modellierung von Mehrphasenströmungen (Ü) 1  
K1302-1H1630Ü Numerical modelling of multiphase flows (E) SuSe 2019
K1302-1H1630Ü Numerical modelling of multiphase flows (E) SuSe 2020
K1104-CMS03V Data Visualization (L) 2  
K1104-CMS03V Data Visualization (L) WiSe 2019/20
K1104-CMS03Ü Data Visualization (E) 2  
K1104-CMS03Ü Data Visualization (E) WiSe 2019/20
K1104-MA0025V Computer Graphics 1 (L) 2  
K1104-MA0025Ü Computer Graphics 1 (E) 2  
K1107-MA0009V Computer Vision 1 (V) 2  
K1107-MA0009Ü Computer Vision 1 (E) 2  
K1107-MA0062V Machine Learning 2 (V) 2  
K1107-MA0062V Machine Learning 2 (L) SuSe 2020
K1107-MA0062Ü Machine Learning 2 (Ü) 2  
K1107-MA0062Ü Machine Learning 2 (E) SuSe 2020
K1202-100030V Numerische Mathematik (V) 2  
K1202-100030V Numerical Mathematics (L) WiSe 2019/20
K1202-100030Ü Numerische Mathematik (Ü) 2  
K1202-100030Ü Numerical Mathematics (E) WiSe 2019/20
K1208-1M0320V Numerische Bauelementesimulation (V) 2  
K1208-1M0320V Numerical Device Simulation (L) WiSe 2020/21
K1208-1M0320Ü Numerische Bauelementesimulation (Ü) 2  
K1208-1M0320Ü Numerical Device Simulation (E) WiSe 2020/21
K1208-500170V Future Computing Strategies in Nano-Electronic Systems (L) 2  
K1208-500170V Theory of Nonlinear Networks (L) WiSe 2019/20
K1208-500170Ü Future Computing Strategies in Nano-Electronic Systems (E) 2  
K1208-500170Ü Theory of Nonlinear Networks (E) WiSe 2019/20
K1208-500170Ü Theory of Nonlinear Networks (E) WiSe 2020/21
K1212-500060V Electromechanical Networks (V) 2  
K1212-500060V Electromechanical Networks (L) WiSe 2019/20
K1212-500060V Electromechanical Networks (L) WiSe 2020/21
K1212-500060Ü Electromechanical Networks (Ü) 2  
K1212-500060Ü Electromechanical Networks (E) WiSe 2019/20
K1212-500060Ü Electromechanical Networks (E) WiSe 2020/21
K1301-EX0190V Gekoppelte Simulation/Echtzeitsimulation (V) 2  
K1301-EX0190V Coupled Simulation/Real Time Simulation (L) WiSe 2019/20
K1301-EX0190V Coupled Simulation/Real Time Simulation (L) WiSe 2020/21

Passing rules
Context Name Description
Global * For information on the module examination please see module description
Global * For information on the module examination please see module description

Requirements
Course / Final module requirements Requirements Compulsory pass Weighting
DK1100-MA001X Dummykurs CMS 1 SWS Assessment No 1
DK1100-MA002X Dummykurs CMS 2 SWS Assessment No 2
DK1100-MA003X Dummykurs CMS 3 SWS Assessment No 3
DK1100-MA004X Dummykurs CMS 4 SWS Assessment No 4
K1100-EX0630V Foundations for Machine Learning (L) Written Examination Foundations of Machine Learning No 2
K1104-CMS03V Data Visualization (L) Written Examination/Oral Assessment Data Visualization No 4
K1104-MA0025V Computer Graphics 1 (L) Written Examination/Oral Assessment Computer Graphics 1 No 4
K1107-MA0006V Particle Methods (L) Written Examination/Oral Assessment Particle Methods No 4
K1107-MA0009V Computer Vision 1 (L) Oral Assessment Computer Vision 1 No 4
K1107-MA0060V Machine Learning 1 (L) Written Examination/Oral Assessment Machine Learning 1 No 4
K1107-MA0062V Machine Learning 2 (L) Written Examination/Oral Assessment Machine Learning 2 No 4
K1202-100030V Numerical Mathematics (L) Written Examination Numerical Mathematics No 3
K1208-1M0320V Numerical Device Simulation (L) Written Examination Physics of Selected Components No 3
K1208-500170V Future Computing Strategies in Nano-Electronic Systems (L) Written Examination/Oral Assessment Theory of Nonlinear Networks No 3
K1208-500180V Modeling of Nanoelectronic Devices (V) Written Examination Modeling and Characterization of Nanoelectronic Devices No 3
K1210-1I0150V Antennas (L) Oral Assessment Antennas No 3
K1210-1I0151V Propagation (L) Oral Assessment Propagation No 3
K1212-500060V Electromechanical Networks (L) Written Examination Electromechanical Networks No 3
K1301-1H0500V System Dynamics (L) Written Examination System Dynamics No 4
K1301-1H1625P Multi Body Systems (P) Assignment Multi Body Systems Yes 0
K1301-EX0190V Coupled Simulation/Real Time Simulation (L) Written Examination Coupled Simulation/Real Time Simulation No 2
K1302-1H0521V Turbulent flows and their modelling (L) Written Examination Turbulent flows and their modelling No 4
K1302-1H1630V Numerical modelling of multiphase flows (L) Written Examination/Oral Assessment Numerical modelling of multiphase flows No 4

Caption
**

For this final module requirement, several combinations of requirements do exist. The passing rules (see above) specify, whether you have to complete one or several requirement combinations.