Dynamical Systems

 The course aims to provide the basic knowledge for formulating, for simple problems arising from the applied sciences, models repesented by finite dimensional (discrete or continuos) dynamical systems. In particular, linear and nonlonear systems will be analyzed. The stability of the equilbrium points, the main bifurcation cases, the existence of strange attractors and fractal systems will be studied. 

The course aims to allow the student to acquire the following skills:

-knowledge and understanding:  knowledge of results and fundamental methods. Skill of understanding problems and to extract the major features. Skill of reading, undertanding and analyzing a subject in the related literature and present it in a clear and accurate way. 

-applying knowledge and understanding: skill of elaborainge new example or solving novel theoretical exsercise, looking for the most appopriate methods and applying them in an appropriate way. 

-making judgements: To be able of devise proposals suited to correctly interprete complex problems in the framework of dynamical systems and their applications.  To be able to formulate autonomously  adequate judgements on the applicablity of mathematical  models to theoretical or real situations. 

-communication skills: skills of presenting arguments, problems, ideas and solutions in mathematical terms with clarity and accuracy and with procedures  suited for the audience, both in an oral and a written form. Skill of clearly motivating the choice of the strategy, method and contents, along with the employed computational tools.

-learning skills: reading and analyzing a subject in the engineering literature involving applied mathematics. To tackle in an autonomuous way the systematic study of arguments not previously treated.  To acquire a degree of autonomy such that the student can be able to start with an autonomuos reserach activity.

Mainly frontal lectures.  Simple study cases will be tackled in a MATLAB environment. 

Theory lessons: 35 hours.

Exercises: 12 hours.

Should teaching be carried out in mixed mode or remotely, it may be necessary to introduce changes with respect to previous statements, in line with the programme planned and outlined in the syllabus.

Learning assessment may also be carried out on line, should the conditions require it.

IMPORTANT: in order to guarantee equal opportunities  to the students with handicap and/or any form of disability, such students may ask to talk to the teacher to program suitable actions. The interested students may also contact  prof. Patrizia Daniele o, the delegate in the Department of Mathematics and Computer Science for students with handicap and/or any form of disability.

1. Examples of dynamical systems:
bank account, harmonic oscillator, simple pendulum, economic growth, population dynamics.

2. Linear dynamical systems. Associated matrix and its eigenvalues. Jordan's canonical forms. Matrix esponential. Linear stability and instability. Representation of the solutions as vectorial fields. Phase portrait. Saddles, nodes, focis, centers, wells, springs. 

3. Nonlinear dynamica systems. Fixed points and their stability/instability. Linearization around a hyperbolic equilibrium point.  Theorems of the stable manifold and of 
Hartman-Grobman. Dynamical systems topologycally equivalent. Examples.  Lyapunov's method.  Theorems of Lyapunov.
Theorem of  Dirichlet-Lagrange. Systems of gradien type. Alpha-limit and omega-limit sets of a trajectory. Hamiltonian systems: main features, Liouville's theorem and recurrence theorem of Poincaré. Limit sets and attractors. 
Limit orbit. Periodic orbits, limit cycles, separators. 
Stablty of limit cycles. Poincaré's map. Homoclinic and heteroclinic orbits. Theorem of Poincaré-Bendixon. Mathematical models of pandemics. 

4.Nonlinear systems, periodicity and caos.  Bifurcation parameters. 
Diagram of bifurcation.  Saddle-node, trancritical, picthfork and Hopf bifurcation. Hopf bifurcation theorem. Lorenz system and strange attractor. 

5. Fractals.  Cantor's set. Fractal dimension. Koch snowflake. Contraction theorem of  Banach-Caccioppoli. Spectral norm. Compact sets and Hausdorff metrics. Iterate function systems. Applications. Fractal dimension.

6. Brief account on Runge Kutta methods for ordinary differential equations and implementation in Matlab. . 

1. Notes of the lecturer. 

2. E. Scheinerman, Invitation to Dynamical Systems, available online: http://www.ams.jhu.edu/∼ers/invite/book.pdf

3. L. Perko, Differential equations and dynamical systems, 3rd ed. - New York: Springer-Verlag, 2001.

4. F. Ganthmaker, Lectures in Analytical Mechanics, MIR 1975

 The exam is only oral.
Learning assessment. Final grades will be assigned by taking into account the following criteria:
Rejected: Basic knowledges have not been acquired. The student is not able to solve simple exercises.
18-23: Basic knowledges have been acquired. The student solves simple exercises and has sufficient communications skills and making judgements.
24-27: All the  knowledges have been acquired. The student solves all the proposed exercises making few errors and has good communications skills and making judgements.
28-30 cum laude: the student has acquired all the contents of the course and is able to explain them fully and connect them with a critical spirit; solves the exercises completely and without errors.

If necessary the exam will be  online.