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Advanced Topics in Modeling and Simulation

Code 11514
Year 1
Semester S1
ECTS Credits 6
Workload OT(15H)
Scientific area Informatics
Mode of delivery Face-to-face instruction (mentoring) and self-learning.
Work placements --
Learning outcomes The main objectives of this course unit are to study advanced aspects in topics related with primitives for computer based simulation and and allow the student to understand the process that is underlying the construction and validation of a computer simulation. At the end of this course unit the student should be able to:
Use high quality computer simulation primitives and understand the whole process inherent to the construction and validation of a computer simulation;
Isolate the events that dominate the situation to be simulated and point out the mechanisms that represent them better in the simulation;
Model a problem, and design, implement and test the computer simulation allowing its study;
Identify the type of simulation that best serves the purposes of the situation under analysis;
Validate computer simulations and obtain values ??with statistical significance from its execution.
Syllabus 1. Study of advanced aspects on topics related to computer simulation primitives:
1.1. Implementation and testing of pseudorandom numbers generators;
1.2. Transformations of sequences of numbers with uniform distribution in numbers with a known empirical or non-uniform distribution;
1.3. Creating specific auto-correlation structures in sequences of values ??generated with a computer.

2. Modeling a problem and analysis of real and random conditions that affect that problem:
2.1. Conditions and events essential to the problem under consideration;
2.2. Mimic the real-time operation of complex systems.

3. Simulation of complex systems:
3.1. Combination of simulation primitives to construct a simulation of a complex system;
3.2. Characterization of the computational complexity and memory requirements inherent in the simulation program;
3.3. Analysis, Interpretation and validation of simulation results.
Main Bibliography Main references
Law, A.M. & Kelton, W.D., 2000. Simulation Modeling and Analysis MacGraw Hil, eds., McGraw-Hill.

P. L’Ecuyer and R. Simard, “TestU01: A C Library for Empirical Testing of Random Number Generators,” ACM Transactions on Mathematical Software, vol. 33, no. 4, p. 22, Agosto de 2007.

D. B. Thomas, W. Luk, P. H. Leong, and J. D. Villasenor, “Gaussian Random Number Generators,” ACM Computing Surveys, vol. 39, no. 4, p. 11, 2007.

Secondary references
Pedro R. M. Inácio, Mário M. Freire, Manuela Pereira, and Paulo Monteiro, “Fast Synthesis of Persistent Fractional Brownian Motion," ACM Transactions on Modelling and Computer Simulation, Universidade da Beira Interior, 2010.

Pedro R. M. Inácio, Branka Lakic, Mário M. Freire, Manuela Pereira, and Paulo P. Monteiro, “The Design and Evaluation of the Simple Self-Similar Sequences Generator,” Elsevier Information Sciences, Vol. 179, Issue 23, pp.4029-4045, 2009.
Planned learning activities and teaching methods The subject of this Course Unit is discussed in theoretical classes with 1 hour of weekly contact, being the study and research effort placed on the student, who should prepare himself/herself for the next class discussion. This study is complemented by the development of three individual practical works, focused not only on the understanding of the theoretical concepts, but also for application in scientific research work. These works are proposed throughout the semester and presented in increasing difficulty. The works are also discussed in the theoretical classes and evaluated, accounting for 60% of the final grade.
Metodologias de Ensino e Critérios de Avaliação A avaliação a esta unidade curricular é feita recorrendo a 4 elementos: um teste escrito de aferição de conhecimentos (T) e três trabalhos individuais (T1, T2 e T3). A nota final (N) resulta da média ponderada das classificações nos vários elementos:
N = 0.30 x T + 0.15 x T1 + 0.15 x T2 + 0.40 x T3.

Os trabalhos individuais consistem no desenvolvimento de propostas discutidas ao longo do semestre, com recurso a algumas das referências bibliográficas sugeridas e que incorporam o desenho e programação de simulações computacionais. Estes trabalhos serão acompanhados de relatórios. O(a) aluno(a) é aprovado por ensino-aprendizagem caso obtenha nota final igual ou superior a 9,5 valores.

A admissão a exame depende da nota final ser igual ou superior a 6 valores. O exame substitui apenas o teste escrito (os trabalhos continuam a contar para a nota após exame), e a aprovação do(a) aluno(a) após exame rege-se novamente de acordo com o que foi estipulado acima para a aprovação durante o período ensino-aprendizagem. Para obter aprovação, o(a) aluno(a) deve ter nota final superior ou igual a 9,5 valores, em que N é dada por
N = 0.30 x E + 0.15 x T1 + 0.15 x T2 + 0.40 x T3,
sendo que E denota a classificação obtida em exame.
Estes critérios aplicam-se a alunos de erasmus e trabalhadores estudantes, à excepção da assiduidade às aulas e salvo situações pontuais devidamente discutidas e acordadas com o regente da unidade curricular.

Momentos de Avaliação Teste Escrito - 19/12/2018
Data limite de entrega do trabalho 1 - 07/11/2018
Data limite de entrega do trabalho 2 - 29/11/2018
Data limite de entrega do trabalho 3 - 07/01/2019
Language Portuguese. Tutorial support is available in English.
Last updated on: 2014-08-07

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