The course focuses on numerical methods of statistical physics. Classes includes lectures about theoretical aspects of Monte-Carlo and molecular dynamics methods, and practical work on computer to solve some statistical physics problems.
As a result of this study course the student acquires basic knowledge about multiphysical modeling, basic steps of problem-solving, verification and analysis of results. The acquired competencies allow to hoose a physical model suitable for the description of the physical process, to explain physical processes on the basis of obtained results and to give recommendations for optimization of the physical process.
The aim of the course is to acquaint students with creation of mathematical models for description of complex physical processes and methods for solving the corresponding problems of mathematical physics, using different program tools – both commercial and specialised mathematical modelling programs.
The course focuses on two methods designed for calculation of physical fields: finite element method (FEM) and boundary element method (BEM). Students learn basics of both methods.
Theoretical lectures are complemented by laboratory work sessions, where students acquire practical skills in the use of the appropriate software.
In addition to the theoretical background students acquire numerical aspects of realization of these methods in computer codes.
Open source software „freefem++” and "gmsh" are used as basic tools to learn FEM and BEM.
The goal of the course is to introduce the students to the basics of data analysis and machine learning methods as an additional tool for finding patterns in data and issuing predictions, by working with data from various physical systems.
The tasks of the course are to introduce the students to the elements of data analysis - cleaning, analysis and visualization, based on data from real physical systems; to apply the machine learning algorithms by mathematically modelling various physical systems.
The aim of the course is to create an insight into high-performance computing in Physics. The tasks of the course are: (1) to overview applications of parallel algorithms in Physics problems, (2) to overview methods of parallel computing, (3) to learn how to use high-performance libraries, (4) to analyse efficiency of parallel algorithms, (5) to gain an experience in using supercomputing centres.
MHD Modelling School brings together professional lecturers, PhD students and open-source simulation software users from the fields of applied magnetohydrodynamics (MHD) and induction heating of metals. It is an intensive hands-on course with the focus on contemporary tools modelling tools for industrial processes. The course also covers experimental methods used for verification of numerical models. Hands-on sessions showcase open-source simulation software: Elmer, GetDP, OpenFOAM, EOF-Library. Pre- and post-processing packages Salome and ParaView are introduced.
As the power and capabilities of computing increases, Artificial Intelligence solutions takes a greater role to perform and execute various processes. Seminar is intended to provide insight into artificial neural networks, give practical examples of deep learning applications and solution implementation using Python and Tensorflow.
Participants will get hands-on experience in implementing deep learning solutions by using Python which currently is one of the most popular programming languages.
As the power and capabilities of computing increases, Artificial Intelligence solutions takes a greater role to perform and execute various processes. Being a part of Artificial Intelligence, Machine Learning provides computer learning and decision-making based on the provided data. Seminar is intended to provide insight into Machine Learning and its algorithms covering supervised and unsupervised learning, including data processing and application for machine learning solutions.
