Syllabus
* 1. Basics of classical and modern computational physics
Main directions of computational physics. Classical and modern computational physics.
* 2. Evolutionary modeling
Basics of the Darwin‘s theory of evolution and evolutionary programming, operators. Encodings - binary, Gray, permutation, value, and others. Fitness. Basic algorithms - blind, hill-climbing, simulated annealing, hill-climbing with learning, tabu search. Genetic algorithms. Evolutionary strategy. Genetic programming. Advanced algorithms of evolutionary modeling. Applications - NP problems, travelling salesman problem, physical applications.
* 3. Advanced techniques of computer modeling
Advanced algorithms of molecular dynamics. Particle-in-Cell method, powerful Poisson equation solvers - conjugate gradients, multigrid methods, LU decomposition, fast fourier transform. Efficient calculation of force interaction - tree algorithms, Ewald summation, fast multipole method. Advanced Monte Carlo method - sampling in statistical physics. Parallelization. Hybrid modeling - various combinations of continuous and particle simulations.
Annotation
The lecture introduces advanced methods for modeling of physical problems and optimization.