Master
2024/2025![Learning Objectives](/f/src/global/i/edu/objectives.svg)
![Expected Learning Outcomes](/f/src/global/i/edu/results.svg)
![Course Contents](/f/src/global/i/edu/sections.svg)
![Assessment Elements](/f/src/global/i/edu/controls.svg)
![Interim Assessment](/f/src/global/i/edu/intermediate_certification.svg)
![Bibliography](/f/src/global/i/edu/library.svg)
Multiscale Modeling and Supercomputer Architectures
Type:
Elective course (Systems Analysis and Mathematical Technologies)
Area of studies:
Applied Mathematics and Informatics
Delivered by:
School of Applied Mathematics
When:
2 year, 1, 2 module
Mode of studies:
offline
Open to:
students of all HSE University campuses
Master’s programme:
Systems Analysis and Mathematical Technologies
Language:
English
ECTS credits:
6
Contact hours:
56
Course Syllabus
Abstract
The course «Computer multiscale modelling and simulation» is aimed at the teaching students with a wide spectrum of methods, technologies and problems in the field of multiscale modelling and simulation and material properties. Different levels of theoretical description at various space and time scales are considered as well as the connections between them and computational technologies oriented on the hardware of the pre-exaflops era supercomputers
Learning Objectives
- The objectives of this course are at the suding of a wide spectrum of methods, technologies and problems in the field of multiscale modelling and simulation and material properties
Expected Learning Outcomes
- be capable of: Application in the given subject area statistical methods of processing experimental data, numerical methods, methods of mathematical and computational modeling of complex systems; Understanding meaning of the tasks appearing in the course of professional activity and employment the related physico-mathematical apparatus for description and solving the above tasks; Using the knowledge of physical and mathematical subjects for further learning according to the training profile;
- get experience in: Formulation of computational tasks in studies of complex systems; Preparing and running computer simulations of various systems; Correct processing of modeling results and their comparison with available experimental and literature data; Theoretical analysis of real problems related to atomic-scale studies
- know: the principles of the theoretical and computational description of matter at various scales. the basic algorithms for application of software for numerical solution of problems at each scale. the principles of bridging the gaps between the scale for solving particular problems and to have the corresponding experience
- be capable of: Estimation the computational complexity of the multiscale problems and the amount of computational resources for their solution; Analyzing scient ific problems and physical processes, realizing in practice fundamental knowledge obtained in the course of training; Adaptation new problematics, knowledge, scientific terminology and methodology, to possess the skills of independent learning;
Course Contents
- Computational aspects of multiscale modelling and simulation
- Principles of bridging the gaps between the scales
- Examples of the development and deployment of multiscale models in different fields
- Multiscale levels of theoretical description of matter
Assessment Elements
- Домашнее задание 1
- Контрольная работа 1
- Домашнее задание 2
- Контрольная работа 2
- Экзамен
Interim Assessment
- 2024/2025 2nd module0.2 * Домашнее задание 1 + 0.2 * Домашнее задание 2 + 0.2 * Контрольная работа 1 + 0.2 * Контрольная работа 2 + 0.2 * Экзамен
Bibliography
Recommended Core Bibliography
- Frenkel D., Smit B. Understanding Molecular Simulation: From Algorithms to Applications. –Elsevier, 2002.
- Rapaport, D. C. The art of molecular dynamics simulation. –Cambridge university press, 2004.
Recommended Additional Bibliography
- Precision Physics of Simple Atomic Systems, S.G. KarshenboimV.B. Smirnov (Eds.), Springer, 2003.
- Schweitzer F., Browning Agents and Active Particles Collective Dynamics in the Natural and Social Sciences, Springer, 2003