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    • Enseignant de la matière : Dr. Ilyes Naidji      Contact :ilyes.naidji@univ-biskra.dz  

      Disponibilité(horaire et lieu) : Laboratoire RLP, université Mohamed Khider Biskra

      Coefficient : 3

      Crédits : 3

      Volume horaire global : 3h par semaine (1 cours magistral + 1 TP)

      Modalité d’évaluation: 3 TPs évalués + un examen final

      Modalité de suivi (calendrier du tutorat): Consultation mensuel pour chaque TP.

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  • Course Objectives

    • This course on modeling and simulation typically aims to achieve several key objectives, each contributing to a comprehensive understanding of the principles, methods, and applications of modeling and simulating dynamic systems. Here are the objectives:

      1. Fundamental Concepts: Introduce students to the fundamental concepts of modeling and simulation, including the nature of dynamic systems, state variables, system dynamics, and the importance of abstraction in modeling.

      2. Mathematical Foundation: Provide students with the mathematical foundations necessary for modeling dynamic systems, including differential equations, difference equations, probability theory, and numerical methods for solving equations.

      3. Modeling Techniques: Teach students various modeling techniques used to represent real-world systems, such as deterministic models (e.g., differential equations, difference equations) and stochastic models (e.g., Markov chains, Monte Carlo simulations).

      4. Simulation Methods: Familiarize students with simulation methods and algorithms used to simulate dynamic systems, including continuous simulation techniques (e.g., numerical integration) and discrete event simulation (e.g., event-driven simulation).

      5. Model Validation and Verification: Teach students techniques for validating and verifying models against empirical data, including sensitivity analysis, parameter estimation, goodness-of-fit tests, and comparison with experimental results.

      6. Software Tools: Introduce students to software tools commonly used for modeling and simulation, such as MATLAB, Simulink, Python, Arena, AnyLogic, or specialized simulation packages depending on the focus of the course.

      7. Applications: Illustrate the diverse applications of modeling and simulation across different domains, including engineering (e.g., control systems, mechanical systems), biology (e.g., population dynamics, biochemical networks), economics (e.g., market simulations, economic forecasting), and social sciences (e.g., agent-based modeling, social network analysis).

      8. Problem-solving Skills: Develop students' problem-solving skills by engaging them in hands-on exercises, projects, and case studies that require them to apply modeling and simulation techniques to real-world problems.

      9. Critical Thinking: Foster critical thinking skills by encouraging students to analyze and interpret simulation results, identify assumptions and limitations of models, and evaluate the implications of different modeling choices.

      10. Communication and Collaboration: Cultivate students' ability to communicate their findings effectively through written reports, presentations, and discussions, and promote collaboration through group projects and peer feedback.

      By achieving these objectives, students gain a solid foundation in modeling and simulation that prepares them to apply these techniques to a wide range of problems in their respective fields and to contribute to the advancement of knowledge and innovation in dynamic systems analysis and design.

  • Prerequisites

    • The prerequisites for this modeling and simulation course can vary depending on the specific focus and level of the course, but typically include a foundation in mathematics, programming, and basic concepts in engineering or a related field. Here's a breakdown of common prerequisites:

      1. Mathematics: A strong understanding of calculus is essential, including differential equations, integral calculus, and multivariable calculus. Linear algebra is also important, as it provides the mathematical framework for many modeling and simulation techniques, such as matrix operations and eigenvalue analysis.

      2. Probability and Statistics: Knowledge of probability theory and statistics is necessary for understanding stochastic processes, random variables, probability distributions, and statistical inference. This background is essential for modeling uncertainty and variability in dynamic systems.

      3. Programming Skills: Proficiency in programming is often required, as modeling and simulation tasks typically involve writing code to implement mathematical models, simulate system behavior, and analyze simulation results. Common programming languages used in modeling and simulation include MATLAB, Python, Java, C++, and R.

      4. Engineering or Science Fundamentals: A solid understanding of basic principles in engineering, physics, biology, economics, or other relevant disciplines is beneficial. This background knowledge provides context for understanding the dynamics of real-world systems and formulating appropriate mathematical models.

      5. Computer Science Concepts: Familiarity with computer science concepts such as algorithms, data structures, and computational complexity can be helpful for implementing efficient simulation algorithms and analyzing large-scale simulation data.

      6. Critical Thinking and Problem-solving Skills: Strong analytical and problem-solving skills are essential for developing and analyzing mathematical models, interpreting simulation results, and making informed decisions based on simulation outcomes.

      7. Prior Coursework: Some modeling and simulation courses may have specific prerequisite courses, such as introductory courses in differential equations, probability theory, or numerical methods. Students are often expected to have completed these prerequisite courses or have equivalent knowledge before enrolling in the modeling and simulation course.

  • Introduction to Modeling and Simulation

  • Set Theory

  • Linear Algebra

  • Calculus

  • Probabilities

  • Modeling and Simulation of Dynamic Systems