Set Reminder Save for later

Computers, Waves, Simulations

A Practical Introduction to Numerical Methods using Python

Interested in learning how to solve partial differential equations with numerical methods and how to turn them into python codes? This course provides you with a basic introduction how to apply methods like the finite-difference method, the pseudospectral method, the linear and spectral element method to the 1D (or 2D) scalar wave equation. The mathematical derivation of the computational algorithm is accompanied by python codes embedded in Jupyter notebooks. In a unique setup you can see how the mathematical equations are transformed to a computer code and the results visualized. The emphasis is on illustrating the fundamental mathematical ingredients of the various numerical methods (e.g., Taylor series, Fourier series, differentiation, function interpolation, numerical integration) and how they compare. You will be provided with strategies how to ensure your solutions are correct, for example benchmarking with analytical solutions or convergence tests. The mathematical aspects are complemented by a basic introduction to wave physics, discretization, meshes, parallel programming, computing models. The course targets anyone who aims at developing or using numerical methods applied to partial differential equations and is seeking a practical introduction at a basic level. The methodologies discussed are widely used in natural sciences, engineering, as well as economics and other fields.

Get Details and Enroll Now

OpenCourser is an affiliate partner of Coursera.

Get a Reminder

Not ready to enroll yet? We'll send you an email reminder for this course

Send to:

Coursera

&

Ludwig-Maximilians-Universität München (LMU)

Rating Not enough ratings
Length 10 weeks
Starts Apr 1 (next week)
Cost $49
From Ludwig-Maximilians-Universität München (LMU) via Coursera
Instructor Heiner Igel
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Engineering Science
Tags Computer Science Algorithms Physical Science And Engineering Research Methods

Get a Reminder

Get an email reminder about this course

Send to:

Careers

An overview of related careers and their average salaries in the US. Bars indicate income percentile.

Open Rank Faculty Position in Quantitative Methods $51k

Staff Analyst, Office Administrative Methods and Analysis $63k

Adjunct Professor - Statistics and Research Methods $69k

Adjunct Professor, TESL Methods $73k

Research and Development Engineer - Numerical Simulation $78k

Numerical Control Analyst $78k

Assistant Adjunct Professor, Research Methods $86k

Methods Engineer (Contingent Labor) $95k

Junior Methods Engineer $102k

Methods / Reliability/ Tech support Manager $102k

Methods and Tools $134k

Principal Training and Methods Coordinator $177k

Write a review

Your opinion matters. Tell us what you think.

Coursera

&

Ludwig-Maximilians-Universität München (LMU)

Rating Not enough ratings
Length 10 weeks
Starts Apr 1 (next week)
Cost $49
From Ludwig-Maximilians-Universität München (LMU) via Coursera
Instructor Heiner Igel
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Engineering Science
Tags Computer Science Algorithms Physical Science And Engineering Research Methods

Similar Courses

Sorted by relevance

Like this course?

Here's what to do next:

  • Save this course for later
  • Get more details from the course provider
  • Enroll in this course
Enroll Now