Systems Modeling and Simulation

Credits
6
Types
Compulsory
Requirements
This subject has not requirements , but it has got previous capacities
Department
EIO
The subject will present the concept of model and the fundamental simulation techniques for the analysis of complex systems.

Teachers

Person in charge

Others

Weekly hours

Theory
1
Problems
1
Laboratory
2
Guided learning
0
Autonomous learning
6

Competences

Transversal Competences

Transversals

  • CT2 [Avaluable] - Sustainability and Social Commitment. To know and understand the complexity of economic and social phenomena typical of the welfare society; Be able to relate well-being to globalization and sustainability; Achieve skills to use in a balanced and compatible way the technique, the technology, the economy and the sustainability.
  • CT5 [Avaluable] - Solvent use of information resources. Manage the acquisition, structuring, analysis and visualization of data and information in the field of specialty and critically evaluate the results of such management.
  • CT6 [Avaluable] - Autonomous Learning. Detect deficiencies in one's own knowledge and overcome them through critical reflection and the choice of the best action to extend this knowledge.
  • CT7 - Third language. Know a third language, preferably English, with an adequate oral and written level and in line with the needs of graduates.

    • Basic

  • CB2 - That the students know how to apply their knowledge to their work or vocation in a professional way and possess the skills that are usually demonstrated through the elaboration and defense of arguments and problem solving within their area of ??study.
  • CB3 - That students have the ability to gather and interpret relevant data (usually within their area of ??study) to make judgments that include a reflection on relevant social, scientific or ethical issues.
  • CB4 - That the students can transmit information, ideas, problems and solutions to a specialized and non-specialized public.

    • Technical Competences

    Especifics

  • CE01 - To be able to solve the mathematical problems that may arise in the field of artificial intelligence. Apply knowledge from: algebra, differential and integral calculus and numerical methods; statistics and optimization.
  • CE20 - To select and put to use techniques of statistical modeling and data analysis, assessing the quality of the models, validating and interpreting.
  • CE21 - To formulate and solve mathematical optimization problems.
  • CE22 - To represent, design and analyze dynamic systems. To acquire concepts such as observability, stability and controllability.

    • Generic Technical Competences

    Generic

  • CG2 - To use the fundamental knowledge and solid work methodologies acquired during the studies to adapt to the new technological scenarios of the future.
  • CG4 - Reasoning, analyzing reality and designing algorithms and formulations that model it. To identify problems and construct valid algorithmic or mathematical solutions, eventually new, integrating the necessary multidisciplinary knowledge, evaluating different alternatives with a critical spirit, justifying the decisions taken, interpreting and synthesizing the results in the context of the application domain and establishing methodological generalizations based on specific applications.
  • CG5 - Work in multidisciplinary teams and projects related to artificial intelligence and robotics, interacting fluently with engineers and professionals from other disciplines.

    • Objectives

    1. Model complex dynamic systems. Understand concepts such as observability, stability and controllability.
      Related competences: CG2, CG4, CT5, CT6, CT7, CB2, CB3, CB4, CE01, CE20, CE21, CE22,
    2. Validate and verify models and extract knowledge from them.
      Related competences: CG2, CG4, CG5, CT2, CB2, CB3, CB4, CE20, CE22,
    3. Express the behavior of complex systems using formal languages understandable by both specialized and non-specialized audiences
      Related competences: CG2, CG4, CG5, CT5, CT6, CB2, CB3, CB4, CE01, CE20,

    Contents

    1. Introduction, system vs model
      What is a simulation study? Practical approach by presenting real projects that will show the student the phases to follow for the development of a valid and useful simulation study. We will address the dichotomy between model and system and understand the need to detail the hypotheses to limit which is what will be the object of our study.
    2. Simulation and statistical methods
      Randomness as the backbone of modeling and experimentation in simulation. Statistical distributions, generation of numbers and random variables. Some known distributions and their application in simulation models.
    3. Simulation paradigms
      Presentation of the main simulation engines and their applicability.
    4. Main formal languages to define conceptual models.
      The languages: Specification and Description Language (SDL) and Petri Nets will be detailed. The relationship they have with the Forrester diagrams used to create dynamic systems will be shown.
    5. Systems dynamics, continuous simulation
      Approach to continuous simulation through systems dynamics, creation of Causal and Forrester diagrams.
    6. Parallel and distributed simulation
      Introduction to existing techniques to be able to distribute simulation models.
    7. Experimental design and analysis of results
      Basic concepts and methods for the design of simulation experiments. Evaluation and comparison of scenarios. Quality of results.
    8. Validation, verification and accreditation of simulation models
      Description of the methodologies to be followed in order to obtain a verified model, validate it and a reflection on the accreditation of models.

    Activities

    Activity Evaluation act


    Basic fundamentals of simulation


    Objectives: 1
    Contents:
    Theory
    1h
    Problems
    1h
    Laboratory
    2h
    Guided learning
    0h
    Autonomous learning
    6h

    Discrete Event Simulation (DES)


    Objectives: 1
    Contents:
    Theory
    2h
    Problems
    2h
    Laboratory
    4h
    Guided learning
    0h
    Autonomous learning
    12h

    Randomness and Simulation


    Objectives: 1
    Contents:
    Theory
    1h
    Problems
    1h
    Laboratory
    2h
    Guided learning
    0h
    Autonomous learning
    4h

    Main formal languages to define conceptual models

    The languages: Specification and Description Language (SDL) and Petri Nets will be detailed. The relationship they have with the Forrester diagrams used to create dynamic systems will be shown.
    Objectives: 3
    Contents:
    Theory
    3h
    Problems
    3h
    Laboratory
    6h
    Guided learning
    0h
    Autonomous learning
    20h

    Experimental design and analysis of results


    Objectives: 2
    Contents:
    Theory
    3h
    Problems
    3h
    Laboratory
    6h
    Guided learning
    0h
    Autonomous learning
    20h

    Systems dynamics, continuous simulation



    Theory
    3h
    Problems
    3h
    Laboratory
    6h
    Guided learning
    0h
    Autonomous learning
    16h

    Validation, verification and accreditation of simulation models


    Objectives: 2
    Contents:
    Theory
    1h
    Problems
    1h
    Laboratory
    2h
    Guided learning
    0h
    Autonomous learning
    8h

    Parallel and distributed simulation



    Theory
    1h
    Problems
    1h
    Laboratory
    2h
    Guided learning
    0h
    Autonomous learning
    4h

    Teaching methodology

    The subject follows the methodologies of cooperative learning and problem-based / project-based learning, complemented with expository method sessions, in which the necessary theory is explained so that the student can develop, in the best conditions. , the set of deliverables that, basically, will determine the achievement of the aims of the asignatura.

    Evaluation methodology

    There will be two practices during the course, 60% of the grade.
    There will be a final exam, 40% of the grade.

    Reassessment: Only those who have failed the final exam may take the reassessment. The maximum grade that can be obtained in the reassessment is 7.

    Bibliography

    Basic

    Complementary

    • Simulation : the practice of model development and use - Robinson, Stewart, Palgrave Macmillan, 2014. ISBN: 9781137328038

    Web links

    Previous capacities

    Notions of statistics and programming.