Physical and mathematical fundamentals for biophysics
Prerequisiti
The course is designed for the first year of the postgraduate course in Quantum Technology and Nanoscience but open to all PhD courses in the Science class of the School, and to students of the ordinary course or other master's degree courses as well (e.g., Materials and Nanotechnologies).
Before the beginning of the course, it is useful to review notions of physics and mathematics even if at high school level (functions of real numbers and their graphs, preferably derivatives and meaning of integrals, vectors in plane and in space, basics of trigonometry, probability, probability distributions; kinetics and dynamics of the material point, forces, potential and kinetic energy, Coulomb force, electrostatics). These topics will in any case be resumed and / or their knowledge will be tested in exercises during the course.
Programma
Basics of mathematics and physics required in strictly quantitative PhDs. The course concerns the use of these tools, not the theoretical basis.
The arguments may include: functions, derivatives, integrals, functions of functions, even with several variables; algebraic groups and fields, vector spaces, hints on matrices, functionals; complex numbers, complex exponential, trigonometry; hints on differential equations and solution of simple linear differential equations; scalar and vector fields: forces, energy, potentials (e.g., Coulomb potential); harmonic motion.
The second half of the course contains practicals in quantum mechanics as well.
Topics can be decided and changed according to students' requests.
Obiettivi formativi
The course aims to recall or teach the fundamentals in Mathematics and Physics required in strictly quantitative PhD courses, such as Biophysics, Nanoscience or Quantum Technology and Nanoscience, to students with possible gaps in these topics.
It is especially dedicated to those who have to face strictly quantitative courses (for example, “Introductory Quantum Physics” or “Fundamentals of Biophysics at the Nanoscale”) having a weak background in these areas.
Riferimenti bibliografici
"An Introduction to Error Analysis", J. R. Taylor