Personal Websites

Alexander Shapeev

The main interest of Alexander Shapeev is the application of mathematical methods to computational materials science. His current research is devoted to developing a mathematical theory and new computational methods for materials defects (such as dislocations or cracks).

Alexander Shapeev holds a Ph.D. degree in Mathematics from National University of Singapore. Prior to joining Skoltech, Alexander was a Postdoctoral Associate in the Department of Mathematics in the University of Minnesota, after an appointment at the Swiss Federal Institute of Technology (EPFL). Prior to that, he was a Research Engineer at the Lavrentyev Institute of Hydrodynamics (SB RAS, Russia).

Alexander Shapeev is an author of 12 peer-reviewed papers, one of which has been awarded the 2013 SIAM Outstanding Paper Prize, in addition to several other graduate-level awards.

Read about my research on my website

Evgeny Podryabinkin
Research Scientist
Ivan Novikov
Junior Research Scientist

Multiscale Methods / Numerical Methods for Partial Differential Equations
Number of ECTS credits: 6
Course Classification: Science, Technology, and Engineering

Course description:
Multiscale modelling is a methodology that considers phenomena at various scales of a problem to achieve better accuracy. Multiscale problems include modelling composites with their microscructure, porous flows (e.g., in oil extraction), or motion of defects in solids. This course will introduce a number of multiscale problems, will review a homogenization method of solving some of such problems, then focus on numerical methods.

The Skoltech’s “Numerical Methods for Partial Differential Equations” (Term 3) is a strongly suggested (but not compulsory) prerequisite and “Introduction to Numerical Simulation” (Term 2) is a suggested prerequisite.

Fast Methods for Partial Differential and Integral Equations
Number of ECTS credits: 6
Course Classification: Science, Technology, and Engineering

Course Description:
The course covers numerical methods for partial differential equations (PDEs). The focus on (a subset of) prototypical examples of elliptic, parabolic, and hyperbolic PDEs, and on the methods of finite differences and finite elements. The students will be exposed to notions of approximation, stability, and accuracy.

Intended Learning Outcomes:
Upon completion of this course, the student will be able to:
For various types of PDEs, propose suitable numerical methods and implement them.
Assess stability and accuracy of numerical methods

ФИО: Шапеев Александр Васильевич

Занимаемая должность (должности): Старший Преподаватель

Преподаваемые дисциплины: Быстрые методы решения дифференциальных и интегральных уравнений

Ученая степень: Ph.D., вычислительная математика, 2009, Национальный Университет Сингапура

Ученое звание (при наличии): нет

Наименование направления подготовки и/или специальности: Вычислительная математика

Данные о повышении квалификации и/или профессиональной переподготовке (при наличии): нет

Общий стаж работы: 13 лет

Стаж работы по специальности: 13 лет