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.
Research generates significant amounts of data that are used to communicate the results of a particular investigation. However, currently, these data are usually unstructured and highly scattered. As a result, this data can not be used to verify, replicate or reanalyze the findings. Moreover, different standards, annotation practices and data formats for data and metadata (if available) might have been used by other researchers, introducing additional heterogeneity and difficulties in later data integration and interpretation.
