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Regular version of the site

Engineering

Computational Methods (2 ECTS credits, 16 academic hours, HSE Moscow)
Dates: July, 2 - 14

 
 This is an introductory course to computational methods. You will get familiar with some concepts and algorithms of computational methods and their realization in modern programming packages. You will be able to realize your calculations and present the results of calculations in 2D and 3D formats. The practical part of the course includes working with Matlab. Programming skills are welcome, but not strongly required. You will get all necessary instruction for the usage of built-in functions and for creating your own user functions in this programming package.

Measuring technique (2 ECTS credits, 16 academic hours, HSE Moscow)
Dates: June, 20 - June, 30

 



The course is oriented for students of 2nd or 3d year, not necessarily physicist, having an affinity with experimental activity. Listeners should have passed the basic courses in mechanics, electricity, and thermodynamics. Lectures start with dimensional analysis,
system of units and standards, a brief overview of error analysis in experiments. The main part of the course covers the technique of measuring basic quantities in physics with particular emphasis on electric properties. Methods of improvement of signal-to-noise ratio are discussed. The course can be considered as introductory to the advanced ones.

Introduction to superconductivity (2 ECTS credits, 16 academic hours, HSE Moscow)
Dates: June, 20 - June, 30



The course is oriented to engineers, graduate and undergraduate students expecting to work in the field. Lectures start from a brief overview of the elementary theory of metals. Phenomenological models of superconductivity, Ginzburg-Landau model, BCS model, type-I and type-II superconductors, high-Tc superconductivity, applications of superconductors. The material is presented minimizing unnecessary math emphasizing underlying physics.

Computing in Sciences, Data Science, and Engineering (2 ECTS credits, 16 academic hours, HSE Moscow)
Dates: July, 9 - 28

Computational methods play a crucial role in modern natural sciences and engineering. This is due to the fact that problems arising in both fundamental research and applications in a variety of subject matter fields --- ranging from molecular biology to neurosciences to quantum chemistry to applied statistics or data science --- are typically quite complicated. Over the last decades, researchers, engineers, and numerical analysts have grown to rely on easily available and ever abundant computing power, as the vast majority of problems in modern-day sciences and engineering cannot be solved without some form of computing.Computing power, however, does not solve problems by itself. Numerical algorithms do. This way, being able to use and develop fast, reliable and robust numerical algorithms is a key skill for working in a variety of subject matter fields.In this course, we will discuss several selected topics, key building blocks of modern computational-science, and practice implementing them using open-source tools of the Scientific Python ecosystem.In particular, we will discuss and work through:
  • Numerical linear algebra. Systems of linear equations. Matrix Decompositions.
  • Iterative methods for systems of linear equations. Large-scale, sparse systems.
  • Looking for optima: minimization and maximization of functions.
  • Interpolation and approximation. Modelling data.

* Please note, all dates TBC