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Module name:
Identification of models and objects
Academic year:
Faculty of:
2012/2013
MIS-2-302-SI-s
ECTS credits:
5
Metals Engineering and Industrial Computer Science
Field of study:
Study level:
Code:
Applied Computer Science
Second-cycle studies
Lecture language:
English
Specialty: Industrial Computer Science
Form and type of study:
Profile of education:
Academic (A)
Full-time studies
Semester:
3
Course homepage:
Responsible teacher:
Szeliga Danuta ([email protected])
Academic teachers:
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)
Student demonstrates the ability to
work in team environment on
developing the solution of inverse
problem.
IS2A_K02, IS2A_K03,
IS2A_K06, IS2A_K07
Activity during classes, Participation
in a discussion, Involvement in
teamwork
M_U001
Student demonstrates the ability to
define the inverse problem and to
solve the defined problem.
IS2A_U01, IS2A_U07,
IS2A_U08, IS2A_U09
Activity during classes, Examination,
Oral answer, Participation in a
discussion, Execution of laboratory
classes, Completion of laboratory
classes
M_U002
Student demonstrates the ability to
seek and obtain the information
from scientific literature.
IS2A_U01
Activity during classes, Participation
in a discussion, Execution of
laboratory classes
Student is familiar with basic
inverse problems in physics and
the methods to solve these
problems.
IS2A_W08
Activity during classes, Examination,
Test, Oral answer, Participation in a
discussion, Execution of laboratory
classes, Completion of laboratory
classes
Social competence
M_K001
Skills
Knowledge
M_W001
1/4
Module card - Identification of models and objects
M_W002
Student knows the basic sensitivity
analysis methods.
IS2A_W08
Activity during classes, Examination,
Test, Oral answer, Participation in a
discussion, Execution of laboratory
classes, Completion of laboratory
classes
FLO matrix in relation to forms of classes
Conversation
seminar
Seminar
classes
Practical
classes
Fieldwork
classes
-
-
+
-
-
-
-
-
-
-
-
M_U001
Student demonstrates the
ability to define the inverse
problem and to solve the
defined problem.
-
-
+
-
-
-
-
-
-
-
-
M_U002
Student demonstrates the
ability to seek and obtain the
information from scientific
literature.
-
-
+
-
-
-
-
-
-
-
-
M_W001
Student is familiar with basic
inverse problems in physics
and the methods to solve
these problems.
+
-
+
-
-
-
-
-
-
-
-
M_W002
Student knows the basic
sensitivity analysis methods.
+
-
+
-
-
-
-
-
-
-
-
Others
E-learning
Project
classes
Student demonstrates the
ability to work in team
environment on developing
the solution of inverse
problem.
Workshops
Laboratory
classes
Form of classes
Auditorium
classes
Student after module
completion has the
knowledge/ knows how to/is
able to
Lectures
MLO code
Social competence
M_K001
Skills
Knowledge
Module content
Lectures
Inverse and identification problems in numerical modeling
Lectures’ content:
Direct and inverse problem. Well- and ill-posed problems. Regularization of inverse
problems. Inverse problems for partial differential equations. Sensitivity analysis
methods. Design of numerical simulations, screening methods, local sensitivity
methods, sampling algorithms, methods based on analysis of variance.
2/4
Module card - Identification of models and objects
Lectures’ schedule:
1. Introduction. Definition of: process model, inverse and direct problem, sensitivity
analysis.
2. Selected terms and definition of functional analysis.
3. Direct and inverse problems. Examples of these problems in physics.
4. Inverse problems and optimization tasks. Goal function definition. Optimization
algorithms for goal function minimization.
5. Well- and ill-posed problems – definition.
6. Regularization of inverse problems: Tikhonov, Landweber methods, discrete
methods.
7. Sensitivity analysis methods. Problem definition and application.
8. Design of experiments, design of numerical simulations.
9. Screening design methods.
10. Local sensitivity analysis methods.
11. Sampling methods.
12. Analysis of variance methods.
Laboratory classes
Solving of inverse problems examples
1. Solving parameters identification and boundary conditions problems in physics.
2. Developing, implementation and testing of:
- inverse algorithm defined as the combination of direct problem numerical solution
and optimization procedure,
- selected algorithms of sensitivity analysis.
Method of calculating the final grade
Arithmetic mean of laboratory and exam grades.
Prerequisites and additional requirements
Fundamentals of math and physics, programming skills, knowledge of numerical and optimization
methods, knowledge of methods and applications of computer systems dedicated processes numerical
simulations.
Recommended literature and teaching resources
1. Kirsch, A., An introduction to the mathematical theory of inverse problems, Springer, 1996.
2. Engl, H.W., Hanke, M., Neubauer A., Regularization of Inverse Problems, Kluwer Academic Publishers,
1996.
3.Colton, D., Ewing, R., Rundell, W., Inverse problems in partial differential equations.
4. M. Kleiber, H. Antunez, T.D. Hien, P. Kowalczyk, Parameter sensitivity in nonlinear mechanics, J. Wiley,
1997.
5. Saltelli, A., Chan, K., Scott, E.M. , Sensitivity analysis, Wiley, 2001.
6. Kusiak, J., Danielewska-Tułecka, A., Oprocha, P., Optymalizacja. Wybrane metody z przykładami
zastosowań, PWN, Warszawa, 2009.
7. Findeisen W., Szymanowski J., Wierzbicki A., Teoria i metody obliczeniowe optymalizacji, PWN,
Warszawa, 1977.
Scientific publications of module course instructors related to the topic of
the module
1. Application of inverse analysis with metamodelling for identification of metal flow stress / Ł.
SZTANGRET, D. SZELIGA, J. KUSIAK, M. PIETRZYK // Canadian Metallurgical Quarterly ; ISSN 0008-4433.
—
2012
vol.
51
no.
4,
s.
440–446.
—
Bibliogr.
s.
446.
—
tekst:
http://www.ingentaconnect.com/content/maney/cmq/2012/00000051/00000004/art00011
2. Creation of statistically similar representative volume element using PL-Grid environment / Łukasz
3/4
Module card - Identification of models and objects
RAUCH, Krzysztof BZOWSKI, Danuta SZELIGA, Maciej PIETRZYK // W: Cracow’12 Grid Workshop :
October 22–24, 2012, Krakow, Poland : proceedings / eds. Marian Bubak, Michał Turała, Kazimierz Wiatr.
— Kraków : Academic Computer Centre CYFRONET AGH, 2012. — ISBN: 978-83-61433-06-4. — S.
101–102. — Bibliogr. s. 102
3. hp-HGS twin adaptive strategy for inverse resistivity logging measurements / Barbara BARABASZ,
Ewa GAJDA, David Pardo, Maciej PASZYŃSKI, Robert SCHAFER, Danuta SZELIGA // W: CMM 2011 :
19extsuperscript{th} international conference on Computer Methods in Mechanics : 9–12 May 2011,
Warsaw, Poland : short papers / eds. A. Borkowski, T. Lewiński, G. Dzierżanowski. — Warsaw : Warsaw
University of Technology. Faculty of Civil Engineering, 2011 + CD. — ISBN: 978-83-7207-943-5. — S.
121–122. — Bibliogr. s. 122, Abstr.. — Toż W: CMM 2011 [Dokument elektroniczny] : 19th international
conference on Computer Methods in Mechanics : 9–12 May 2011, Warsaw, Poland. — Wersja do
Windows. — Dane tekstowe. — [Warsaw : WUT, 2011]. — 1 dysk optyczny. — S. [1–2]. — Wymagania
systemowe: Adobe Acrobat Reader ; napęd CD-ROM. — Bibliogr. s. [2], Abstr.
4. Identification problems in metal forming : a comprehensive study — Zagadnienie identyfikacji
parametrów modeli procesów przeróbki plastycznej w ujęciu kompletnym / Danuta SZELIGA. — Krakow :
AGH University of Science and Technology Press, 2013. — 145, [1] s.. — (Rozprawy Monografie /
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie ; ISSN 0867-6631 ; 291). — Bibliogr. s.
136-[146], Summ., Streszcz.. — ISBN: 978-83-7464-625-3
5. Identification on material properties based on rolling process at 4-stand laboratory mill / Danuta
SZELIGA, Marcel Graf, Rudolf Kawalla, Maciej PIETRZYK // W: ESAFORM 2011 : The 14th international
conference on Material Forming : 27–29 April 2011, Belfast : book of abstracts / Queen’s University
Belfast. — [Belfast : QUB], [2011]. — Opis wg okł.. — S. 147
Pozostałe:
http://www.bpp.agh.edu.pl/autor/szeliga-danuta-04360
Additional information
None
Student workload (ECTS credits balance)
Student activity form
Student workload
Examination or Final test
2h
Realization of independently performed tasks
25 h
Preparation for classes
30 h
Preparation of a report, presentation, written work, etc.
20 h
Participation in laboratory classes
30 h
Participation in lectures
30 h
Summary student workload
137 h
Module ECTS credits
5 ECTS
4/4