The e-course based on the workshop lectures is available on the IAEA e-learning platform 'CLP4NET' under this URL: https://elearning.iaea.org/m2/course/index.php?categoryid=250
It can be accessed from the button Click to enter this course and upon registration on the IAEA NUCLEUS platform.
Computational science and engineering applied to the field of nuclear science, technology and applications, is tightly related to the study and implementation of numerical analysis, codes and data libraries to address complex physics and engineering problems. With the advancement of computational resources, young nuclear scientists and engineers are encouraged to adopt a variety of tools, including multi-physics and multi-scale approaches in various plasma codes, first-principles calculations, molecular dynamics and Monte Carlo simulations, rate theories, dislocation dynamics, coupled thermal hydraulics and neutronics, structural mechanics and finite element/difference/volume methodologies. In addition, there is an increasing need for understanding computational methods, including advanced modelling and simulation techniques, algorithms, data science methods like machine learning and data mining, deep learning, artificial intelligence, and high performance computing. Integrating high performance computing to mathematical modelling, numerical algorithms and large-scale databases of observations is leading a new paradigm in science and engineering.
The event – through its interdisciplinary programme of lectures – aims to provide students, young researchers, and young professionals with critical skills and tools in areas such as mathematical techniques for modelling and simulation of complex systems, high performance computing, and computational methods for processing and analysing large data sets, applied in nuclear science and engineering.
The event aims to bring together students, young nuclear scientists and engineers, with a strong interest in the development and implementation of modelling and simulations techniques in nuclear science and engineering, as well as in the development and implementation of computational methods, such as machine learning and high performance computing, for complex nuclear physics and engineering systems.