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Computers, Waves, Simulations

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.

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Rating 4.8★ based on 43 ratings
Length 10 weeks
Starts Jul 17 (41 weeks ago)
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

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What people are saying

numerical methods

A nice course for learning numerical methods.

A great course for anyone interested in numerical methods applied to the wave equation.

GOOD Had a great time learning the concepts of numerical methods and how to apply them using python.

This course is by far the best Numerical Methods MOOC course .

meaningful introduction to different sort of numerical methods that could help to solve different real problems, it is really notewhorthy that is very dynamic and entertaining.

One of the best things about this course is the professor's elegant and lucid explanation of difficult concepts of numerical methods during his scintillating lectures.

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introduction to numerical

Overall a good introduction to numerical methods without too many complications, but you do get a feel for how complicated it could quickly become.

This course is a great introduction to numerical methods.I hope that Dr. Igel will provide more courses on writing effective code for numerical methods.

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jupyter notebooks

Incredibly good jupyter notebooks.

Concepts are explained simply and the Jupyter notebooks are a lot of fun to play with.

Then there are programming exercises where you can run simulations in Python (using Jupyter Notebooks).

The Jupyter notebooks are a great format for these sorts of demonstrations.

And the integration of properly commented Jupyter notebooks justified it's name as to "A practical introduction......".

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very well

All concepts are very well explained .

Very well structured.

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wave propagation

Would highly recommend to anyone who is interested in numerical modeling of wave propagation.

It has the perfect amount of theory and practice of seismic wave propagation.

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heiner igel

Heiner Igel is an excellent teacher and he stops Just before the real complications begin as he should at this introduction level.

The format of the course is such that Heiner Igel explains to the viewer while hand-written equations and drawings appear absolutely synchronized in the background.

Dr. Heiner Igel

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dr. igel

Thank you for the effort you put into its development, Dr. Igel.

I struggled with numerical methods as an engineering undergraduate, but Dr. Igel did a fantastic job of presenting and explaining the material.

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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

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Rating 4.8★ based on 43 ratings
Length 10 weeks
Starts Jul 17 (41 weeks ago)
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

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