Modeling and Simulation
A.Y. 2019/2020
Learning objectives
The course aims to provide an overview of modeling, through the basic concepts of systems analysis, especially linear ones. It will provide the elements needed to understand how the models can be used in simulation, forecasting, planning and management, and how they can be integrated to support decision-making. During the course examples and case studies will be analyzed and the analysis will be extended to non-linear models. Computer laboratory sessions, using generic and specific software, will be provided.
Expected learning outcomes
At the end of the course the student will be able to understand/have acquired:
- Role of information and models in decision-making.
- General knowledge on descriptive modeling.
- Problems and techniques for model simulation.
- Parametric identification of linear models.
- General knowledge on modeling decision-making.
- Some troubleshooting techniques for mathematical programming problems, also with many objectives and in uncertain context.
- Role of information and models in decision-making.
- General knowledge on descriptive modeling.
- Problems and techniques for model simulation.
- Parametric identification of linear models.
- General knowledge on modeling decision-making.
- Some troubleshooting techniques for mathematical programming problems, also with many objectives and in uncertain context.
Lesson period: First semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Lesson period
First semester
Course syllabus
* Introduction and reminders: - the representation of reality: physical systems with mathematical models; - general concepts of systems analysis: characteristics, use and limitations of models; - Reminders of mathematics and linear algebra.
* The descriptive models: - generalities and classification; - state, input and output, balance and stability, trajectory and motion. - linear systems: representation, study of equilibrium and stability.
* Identification, simulation and prediction: - identification of a model: calibration and validation - simulation of a model and some examples of software; - methods of discretization of continuum models, basics of computing, dynamic error; - IN-OUT representation of linear systems, class ARMAX predictors.
* Some special aspects: - cellular automa and neural networks; - analysis of data and signals: sampling and filtering.
* The decision models: - generalities and classification; - linear programming: formulation and geometrical interpretation; - multi-objectives analysis: Pareto efficiency and methods of choice; - decisions in uncertain environment: selection criteria, Bayes theorem, decision tree.
* Case planning and management: - discharge of pollutants; - agricultural plan of Sinai; - management of an environmental resource.
* The descriptive models: - generalities and classification; - state, input and output, balance and stability, trajectory and motion. - linear systems: representation, study of equilibrium and stability.
* Identification, simulation and prediction: - identification of a model: calibration and validation - simulation of a model and some examples of software; - methods of discretization of continuum models, basics of computing, dynamic error; - IN-OUT representation of linear systems, class ARMAX predictors.
* Some special aspects: - cellular automa and neural networks; - analysis of data and signals: sampling and filtering.
* The decision models: - generalities and classification; - linear programming: formulation and geometrical interpretation; - multi-objectives analysis: Pareto efficiency and methods of choice; - decisions in uncertain environment: selection criteria, Bayes theorem, decision tree.
* Case planning and management: - discharge of pollutants; - agricultural plan of Sinai; - management of an environmental resource.
Prerequisites for admission
Basic math, basic computer skills
Teaching methods
The course includes lectures, traditional and interactive exercises, thematic seminars, laboratory activities in the computer room, exam simulations.
Teaching Resources
* References: - Lecture notes. - Feature articles suggested during the course. - Exercises: G. Guariso and E. Weber, Analisi e Simulazione dei Modelli, Esculapio, Bologna, 2003.
* Other useful material: - Site of prof. Guariso: http://www.elet.polimi.it/upload/guariso - S. Rinaldi and C. Piccardi, I sistemi lineari: teoria, modelli, applicazioni, CittàStudi Edizioni.
* Other useful material: - Site of prof. Guariso: http://www.elet.polimi.it/upload/guariso - S. Rinaldi and C. Piccardi, I sistemi lineari: teoria, modelli, applicazioni, CittàStudi Edizioni.
Assessment methods and Criteria
The exam consists of three quantitative numerical exercises on the course arguments: at least one on the descriptive part and at least one on the decision-making part.
In addition to the three exercises, students will have to solve a simulation of laboratory activities and will have to answer four open questions on the theoretical part.
In addition to demonstrating knowledge of the subject, the clarity, conciseness, and order in which solutions and responses are justified are assessed.
In addition to the three exercises, students will have to solve a simulation of laboratory activities and will have to answer four open questions on the theoretical part.
In addition to demonstrating knowledge of the subject, the clarity, conciseness, and order in which solutions and responses are justified are assessed.
ING-INF/04 - SYSTEMS AND CONTROL ENGINEERING - University credits: 6
Practicals: 24 hours
Lessons: 36 hours
Lessons: 36 hours
Professor:
Weber Enrico
Shifts:
-
Professor:
Weber Enrico