Introduction to Computational Modelling
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.
High-Performance Computing in Physics
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.
Darbo su superkompiuteriu mokymai: prisijungimas, uždavinių leidimas, kompiliavimas (HPC Saulėtekis)
Online: Using CSC HPC environment efficiently
Online: Using CSC HPC environment efficiently
Using CSC HPC Environment Efficiently
MHD Modelling School 2019
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.