The course objective is to provide to students suitable lectures about scientific computing and procedures for electric and electronics applications, in order to understand and to design simulation software.
teacher profile teaching materials
• An introduction to the MATLAB (OCTAVE) environment
• Representation of real numbers with the calculator
• Calculation of zeros of a function
• Mathematical models and numerical errors
• Approximation of functions and data
• Numerical derivative
• Numerical integral
• numerical methods for ordinary differential equations (ODE)
SECOND PART: ALGORITHMS AND OPTIMIZATION MODELS
• The system concept
• Linear and nonlinear systems
• Abstract models and optimization models
• Mathematical models and models of mathematical optimization
• Optimization algorithms
• Multi-objective optimization
• Linear optimization
• Geometric aspects of the linear optimization
• Nonlinear optimization
• Geometric aspects of the nonlinear optimization
• Local and global optimum
• Main methods of nonlinear optimization
• Heuristic methods for the optimization: Tabu Search, Simulating Annealing, Genetic Algorithms, Bacterial Chemotaxis Algorithm, Particle Swarm
Optimization, Flock Of Starling Optimization
• Artificial Neural Networks
• Applications:
• Design of a circuit simulator
• Simulation of devices with ferromagnetic core
• Load Flow Analysis
Programme
FIRST PART: CALCULATION TOOLS• An introduction to the MATLAB (OCTAVE) environment
• Representation of real numbers with the calculator
• Calculation of zeros of a function
• Mathematical models and numerical errors
• Approximation of functions and data
• Numerical derivative
• Numerical integral
• numerical methods for ordinary differential equations (ODE)
SECOND PART: ALGORITHMS AND OPTIMIZATION MODELS
• The system concept
• Linear and nonlinear systems
• Abstract models and optimization models
• Mathematical models and models of mathematical optimization
• Optimization algorithms
• Multi-objective optimization
• Linear optimization
• Geometric aspects of the linear optimization
• Nonlinear optimization
• Geometric aspects of the nonlinear optimization
• Local and global optimum
• Main methods of nonlinear optimization
• Heuristic methods for the optimization: Tabu Search, Simulating Annealing, Genetic Algorithms, Bacterial Chemotaxis Algorithm, Particle Swarm
Optimization, Flock Of Starling Optimization
• Artificial Neural Networks
• Applications:
• Design of a circuit simulator
• Simulation of devices with ferromagnetic core
• Load Flow Analysis
Core Documentation
QUARTERONI ALFIO; SALERI FAUSTO - CALCOLO SCIENTIFICO. ESERCIZI E PROBLEMI RISOLTI CON MATLAB E OCTAVE - ED. SPRINGER VERLAG ------ VERCELLIS CARLO - OTTIMIZZAZIONE. TEORIA, METODI, APPLICAZIONI - ED. MCGRAW-HILL COMPANIESReference Bibliography
---Type of delivery of the course
Frontal lessons Computer simulationsType of evaluation
Presentation of a research on themes parallel to those covered in the course. Oral examination