Credits: 5

Schedule: 07.01.2020 - 19.05.2020

Teacher in charge (valid 01.08.2018-31.07.2020): 

Ville Havu, Ilja Makkonen (2018-2019)

Ville Havu, Emppu Salonen (2019-2020)

Contact information for the course (applies in this implementation): 

Lecturers:

Emppu Salonen (first half)
Ville Havu (second half)

Course assistant: Filippo Zonta


Teaching Period (valid 01.08.2018-31.07.2020): 

III - V Spring (2018-2019, 2019-2020)

Learning Outcomes (valid 01.08.2018-31.07.2020): 

After completing the course, the student

  • can identify and describe basic models and simulation techniques that are frequently used to solve physical problems in various fields of computational physics, such as quantum, statistical, condensed matter or materials physics,
  • is familiar with the basic operating principles of pseudo random number generators and able to choose one and use it appropriately
  • can apply importance sampling Monte Carlo simulations and the Metropolis algorithm for numerical integration and sampling in physical applications
  • can describe the basic principles of stochastic simulations in statistical physics and apply them to select models and problems
  • can describe the basic principles of molecular dynamics simulations and implement and run simple simulations
  • can describe and categorize models and techniques used in computational single-particle and many-body quantum physics
  • can implement a solver for a physical problem governed by a partial differential equation
  • can choose between different spatial discretizations, methods in linear algebra and time propagation schemes based on the physical properties of the underlying problem
  • can describe modern computing architectures and programming tools for parallel high-performance computing

Content (valid 01.08.2018-31.07.2020): 

Familiarizing with various models appearing in computational quantum, statistical condensed matter and materials physics. Random number generators, stochastic simulation techniques, importance sampling Monte Carlo and the Metropolis algorithm. Molecular dynamics simulations. Basis function and finite-difference discretizations of equations arising in physics, direct and iterative methods of linear algebra to solve discretized equations, explicit and implicit time propagation schemes for time-dependent physical problems.

Assessment Methods and Criteria (valid 01.08.2018-31.07.2020): 

Assignments

Elaboration of the evaluation criteria and methods, and acquainting students with the evaluation (applies in this implementation): 

Grade: 70% from the weekly homework, 30% project

Workload (valid 01.08.2018-31.07.2020): 

Contact teaching: 48 hrs
Independent work: 85 hrs

Details on calculating the workload (applies in this implementation): 

contact teaching 48 hrs (24 hrs lectures + 24 hrs exercises = 12 weeks with 2 hrs lectures and 2 hrs exercises each), independent work 82 hrs

Study Material (valid 01.08.2018-31.07.2020): 

Lecture notes and additional supporting material

Substitutes for Courses (valid 01.08.2018-31.07.2020): 

This course will replace the course Tfy-3.4423

Course Homepage (valid 01.08.2018-31.07.2020): 

https://mycourses.aalto.fi/course/search.php?search=PHYS-E0412

Grading Scale (valid 01.08.2018-31.07.2020): 

0-5

Registration for Courses (valid 01.08.2018-31.07.2020): 

registration via WebOodi.

Details on the schedule (applies in this implementation): 

Lectures: Tuesdays at 10:15-12:00, Y229a, Otakaari 1
Exercise sessions: Thursdays at 10:15-12:00, Maari B, Sähkömiehentie 3

Opening lecture: Tuesday 7 January
First exercise session: Thursday 9 January

Description

Registration and further information