Ludwig-Maximilians-Universität München (LMU)
Computers, Waves, Simulations: A Practical Introduction to Numerical Methods using Python
Ludwig-Maximilians-Universität München (LMU)

Computers, Waves, Simulations: A Practical Introduction to Numerical Methods using Python

Taught in English

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23,860 already enrolled

Course

Gain insight into a topic and learn the fundamentals

Heiner Igel

Instructor: Heiner Igel

4.7

(351 reviews)

Intermediate level

Recommended experience

35 hours to complete
3 weeks at 11 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • How to solve a partial differential equation using the finite-difference, the pseudospectral, or the linear (spectral) finite-element method.

  • Understanding the limits of explicit space-time simulations due to the stability criterion and spatial and temporal sampling requirements.

  • Strategies how to plan and setup sophisticated simulation tasks.

  • Strategies how to avoid errors in simulation results.

Details to know

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Assessments

9 quizzes

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There are 9 modules in this course

The use of numerical methods to solve partial differential equations is motivated giving examples form Earth sciences. Concepts of discretization in space and time are introduced and the necessity to sample fields with sufficient accuracy is motivated (i.e. number of grid points per wavelength). Computational meshes are discussed and their power and restrictions to model complex geometries illustrated. The basics of parallel computers and parallel programming are discussed and their impact on realistic simulations. The specific partial differential equation used in this course to illustrate various numerical methods is presented: the acoustic wave equation. Some physical aspects of this equation are illustrated that are relevant to understand its solutions. Finally Jupyter notebooks are introduced that are used with Python programs to illustrate the implementation of the numerical methods.

What's included

6 videos1 reading1 quiz1 ungraded lab

In Week 2 we introduce the basic definitions of the finite-difference method. We learn how to use Taylor series to estimate the error of the finite-difference approximations to derivatives and how to increase the accuracy of the approximations using longer operators. We also learn how to implement numerical derivatives using Python.

What's included

8 videos1 quiz3 ungraded labs

We develop the finite-difference algorithm to the acoustic wave equation in 1D, discuss boundary conditions and how to initialize a simulation example. We look at solutions using the Python implementation and observe numerical artifacts. We analytically derive one of the most important results of numerical analysis – the CFL criterion which leads to a conditionally stable algorithm for explicit finite-difference schemes.

What's included

9 videos1 quiz2 ungraded labs

We develop the solution to the 2D acoustic wave equation, compare with analytical solutions and demonstrate the phenomenon of numerical (non-physical) anisotropy. We extend the von Neumann Analysis to 2D and derive numerical anisotropy analytically. We learn how to initialize a realistic physical problem and illustrate that 2D solution are already quite powerful to understand complex wave phenomena. We introduced the 1D elastic wave equation and show the concept of staggered-grid schemes with the coupled first-order velocity-stress formulation.

What's included

10 videos1 quiz5 ungraded labs

We start with the problem of function interpolation leading to the concept of Fourier series. We move to the discrete Fourier series and highlight their exact interpolation properties on regular spatial grids. We introduce the derivative of functions using discrete Fourier transforms and use it to solve the 1D and 2D acoustic wave equation. The necessity to simulate waves in limited areas leads us to the definition of Chebyshev polynomials and their uses as basis functions for function interpolation. We develop the concept of differentiation matrices and discuss a solution scheme for the elastic wave equation using Chebyshev polynomials.

What's included

9 videos1 quiz4 ungraded labs

We introduce the concept of finite elements and develop the weak form of the wave equation. We discuss the Galerkin principle and derive a finite-element algorithm for the static elasticity problem based upon linear basis functions. We also discuss how to implement boundary conditions. The finite-difference based relaxation method is derived for the same equation and the solution compared to the finite-element algorithm.

What's included

5 videos1 quiz1 ungraded lab

We extend the finite-element solution to the elastic wave equation and compare the solution scheme to the finite-difference method. To allow direct comparison we formulate the finite-difference solution in matrix-vector form and demonstrate the similarity of the linear finite-element method and the finite-difference approach. We introduce the concept of h-adaptivity, the space-dependence of the element size for heterogeneous media.

What's included

7 videos1 quiz1 ungraded lab

We introduce the fundamentals of the spectral-element method developing a solution scheme for the 1D elastic wave equation. Lagrange polynomials are discussed as the basis functions of choice. The concept of Gauss-Lobatto-Legendre numerical integration is introduced and shown that it leads to a diagonal mass matrix making its inversion trivial.

What's included

7 videos1 quiz2 ungraded labs

We finalize the derivation of the spectral-element solution to the elastic wave equation. We show how to calculate the required derivatives of the Lagrange polynomials making use of Legendre polynomials. We show how to perform the assembly step leading to the final solution system for the elastic wave equation. We demonstrate the numerical solution for homogenous and heterogeneous media.

What's included

7 videos1 quiz2 ungraded labs

Instructor

Instructor ratings
4.9 (125 ratings)
Heiner Igel
Ludwig-Maximilians-Universität München (LMU)
1 Course23,860 learners

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4.7

351 reviews

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