Module also offered within study programmes:
General information:
Name:
Uncertainty analysis in engineering
Course of study:
2019/2020
Code:
ZSDA-3-0083-s
Faculty of:
Szkoła Doktorska AGH
Study level:
Third-cycle studies
Specialty:
-
Field of study:
Szkoła Doktorska AGH
Semester:
0
Profile of education:
Academic (A)
Lecture language:
English
Form and type of study:
Full-time studies
Responsible teacher:
dr hab. inż. Gallina Alberto (agallina@agh.edu.pl)
Dyscypliny:
Moduł multidyscyplinarny
Module summary

The course gives students insight into the problem of uncertainty explaining what tools can be adopted to work in a condition of limited knowledge by theoretical and practical work.

Description of learning outcomes for module
MLO code Student after module completion has the knowledge/ knows how to/is able to Connections with FLO Method of learning outcomes verification (form of completion)
Social competence: is able to
M_K001 Awareness of the responsibility for own work and readiness to comply with the rules of team work and accepting responsibility for tasks performed collectively SDA3A_K01 Execution of laboratory classes
Skills: he can
M_U001 Improving software programming skills and ability to integrate different simulation environments SDA3A_U06, SDA3A_U02 Execution of laboratory classes,
Execution of exercises,
Execution of a project
M_U002 Student is able to present his own work and justify his/her choices made in the execution of the work. SDA3A_U02 Presentation
Knowledge: he knows and understands
M_W001 Awareness of the importance of uncertainty analysis in engineering problems. Understanding of the most common non-deterministic methods and optimization methods used in engineering. SDA3A_W03, SDA3A_W02 Test,
Project,
Presentation,
Execution of laboratory classes,
Execution of a project,
Completion of laboratory classes
Number of hours for each form of classes:
Sum (hours)
Lecture
Audit. classes
Lab. classes
Project classes
Conv. seminar
Seminar classes
Pract. classes
Zaj. terenowe
Zaj. warsztatowe
Prace kontr. przejść.
Lektorat
45 15 0 15 15 0 0 0 0 0 0 0
FLO matrix in relation to forms of classes
MLO code Student after module completion has the knowledge/ knows how to/is able to Form of classes
Lecture
Audit. classes
Lab. classes
Project classes
Conv. seminar
Seminar classes
Pract. classes
Zaj. terenowe
Zaj. warsztatowe
Prace kontr. przejść.
Lektorat
Social competence
M_K001 Awareness of the responsibility for own work and readiness to comply with the rules of team work and accepting responsibility for tasks performed collectively - - + + - - - - - - -
Skills
M_U001 Improving software programming skills and ability to integrate different simulation environments - - + - - - - - - - -
M_U002 Student is able to present his own work and justify his/her choices made in the execution of the work. - - - + - - - - - - -
Knowledge
M_W001 Awareness of the importance of uncertainty analysis in engineering problems. Understanding of the most common non-deterministic methods and optimization methods used in engineering. + - - - - - - - - - -
Student workload (ECTS credits balance)
Student activity form Student workload
Summary student workload 136 h
Module ECTS credits 5 ECTS
Udział w zajęciach dydaktycznych/praktyka 45 h
Preparation for classes 30 h
przygotowanie projektu, prezentacji, pracy pisemnej, sprawozdania 40 h
Realization of independently performed tasks 15 h
Examination or Final test 1 h
Contact hours 5 h
Module content
Lectures (15h):
  1. Introduction

    • General concepts/definitions
    • Design and Modelling process
    • Uncertainty classification
    • Uncertainty descriptors
    • Uncertainty analyses

  2. Optimization

    • Local methods (Newton / Quasi Newton / Newton-Gauss / Neadler Mead / Pattern search)
    • Global methods (Simulating Annealing / Genetic Algorithm / Differential Evolution)

  3. Regression models

    • Linear regression
    • Bayesian linear regression
    • Gaussian process linear regression
    • Neural Networks

  4. Sensitivity analysis

    • Regression analysis
    • Morris method
    • Sobol method
    • Variance based methods
    • Other methods

  5. Reliability analysis

    • First order reliability method
    • Important sampling
    • Monte Carlo method

  6. Propagation of uncertainty

    • Analytic method
    • First Order Second Moment
    • Monte Carlo method
    • Sampling strategies

Laboratory classes (15h):
  1. Fundamentals of calculus of probability

    • Basic concepts
    • Discrete and continuous random variables
    • Fundamental properties
    • Conditional properties
    • Important distributions
    • Maximum likelihood estimator
    • Law of large numbers
    • Central limit theorem


    • Creation of MATALB scripts for testing properties of random theory
    • Creation of MATLAB scripts implementing learned methods

Project classes (15h):
  1. Development of the project

    • Selection of the model and analysis
    • Implementation in DAKOTA or MATLAB
    • Presentation of results

  2. Introduction to Dakota software

    • Fundamentals of Dakota
    • Getting started
    • Interfacing with external software

Additional information
Teaching methods and techniques:
  • Lectures: Nie określono
  • Laboratory classes: Nie określono
  • Project classes: Nie określono
Warunki i sposób zaliczenia poszczególnych form zajęć, w tym zasady zaliczeń poprawkowych, a także warunki dopuszczenia do egzaminu:

At the mid of the course a short test on “Calculus of Probability” will be held.
This gives 30% of the final grade.

The 60% of the final grade is given by the group’s project. In the assessment of the project the following aspects will be considered:

  • Participation to the project
  • Presentation of the project
  • Additional questions given at the project’s presentation

The remaining 10% of the final grade is provided by the student’s attendance

Participation rules in classes:
  • Lectures:
    – Attendance is mandatory: No
    – Participation rules in classes: Nie określono
  • Laboratory classes:
    – Attendance is mandatory: Yes
    – Participation rules in classes: Nie określono
  • Project classes:
    – Attendance is mandatory: Yes
    – Participation rules in classes: Nie określono
Method of calculating the final grade:

At the mid of the course a short test on “Calculus of Probability” will be held.
This gives 30% of the final grade.

The 60% of the final grade is given by the group’s project. In the assessment of the project the following aspects will be considered:

  • Participation to the project
  • Presentation of the project
  • Additional questions given at the project’s presentation

The remaining 10% of the final grade is provided by the student’s attendance

Sposób i tryb wyrównywania zaległości powstałych wskutek nieobecności studenta na zajęciach:

Notes and presentations of the material presented in the lesson will be provided

Prerequisites and additional requirements:
  • Fundamentals of MATLAB
Recommended literature and teaching resources:

Basic:

  • Notes provided by the lecturer:
    Additional:
  • Grinstead, Introduction to probability
  • Meyers and Montgomery, Applied statistics and probability for engineers
  • Bishop, Pattern recognition and machine learning.
Scientific publications of module course instructors related to the topic of the module:

Additional scientific publications not specified

Additional information:

None