FYSA1130 Numerical Methods in Physics (4 cr)
Description
- Integrated development environment (Spyder), debugging, good practices in development
Numerical integrals and derivatives.
Solving linear and non-linear equations numerically.
Solve ordinary- and partial differential equations numerically.
Differences between boundary-, initial- and eigenvalue problems.
Numerical optimization and fitting
Limitations of numerics and estimating errors
Learning outcomes
Upon completion of the course the student is able to
- use a modern development tool for numerical problems
calculate numerical derivatives and integrals
solve simultanous linear and non-linear equations numerically
solve ordinary differential equation numerically
solve elliptic partial differential equation numerically
deal with boundary-, initial- and eigenvalue problems.
use different methods for non-linear optimization
fit linear and non-linear models to data.
Assess the accuracy and precision of numerical solutions
Additional information
Own laptop for use during the lectures and during the exam is highly recommended but not mandatory.
Uses a web platform, such as TIM, where Python code can be run and returned for evaluation.
Description of prerequisites
Reasonable knowledge of high school level physics and mathematics
Some familiarity with concepts relating to differential equations
Basic level Python programming skills
Familiarity with NumPy, SciPy and matplotlib libraries
Study materials
Lecture notes, course TIM-pages
Python language documentation
NumPy, SciPy and matplotlib documentation
Literature
- Newman, Computational Physics
Completion methods
Method 1
Method 2
Method 3
Teaching (4 cr)
Luennot, harjoitustehtävät, ohjaukset ja tentti.
Teaching
9/3–11/22/2024 Lectures
1/9–3/28/2025 Lectures
Independent study (4 cr)
Self-study, home exam and an assessment discussion.
Teaching
9/2/2024–7/31/2025 Independent project
Independent study (4 cr)
Self-Study, project work.