Scientific Computing Lab

Slides:



Advertisements
Similar presentations
Technische Universität München Fakultät für Informatik Scientific Computing in Computer Science Practical Course CFD - Free Boundary Value Problems Tobias.
Advertisements

Lab Course CFD Preliminay Discussion Dr. Miriam Mehl Institut für Informatik Schwerpunkt Wissenschaftliches Rechnen.
Practical Course SC & V Free Boundary Value Problems Dr. Miriam Mehl Institut für Informatik Scientific Computing in Computer Science.
Practical Course SC & V More on Boundaries Dr. Miriam Mehl Institut für Informatik Schwerpunkt Wissenschaftliches Rechnen.
Practical Course SC & V Free Boundary Value Problems Prof. Dr. Hans-Joachim Bungartz Institut für Informatik Schwerpunkt Wissenschaftliches Rechnen.
Institut für Informatik Scientific Computing in Computer Science Practical Course SC & V Time Discretisation Dr. Miriam Mehl.
Explicit vs Implicit. Explicit: Explicit: A function defined in terms of one variable. y= 3x + 2 is defined in terms of x only. Implicit: Implicit: A.
Scientific Computing Lab Results Worksheet 3 Dr. Miriam Mehl Institut für Informatik Scientific Computing in Computer Science.
Solving Equations = 4x – 5(6x – 10) -132 = 4x – 30x = -26x = -26x 7 = x.
ECE602 BME I Partial Differential Equations in Biomedical Engineering.
AE/ME 339 Computational Fluid Dynamics (CFD) K. M. Isaac Professor of Aerospace Engineering.
Implicit Functions The equation y = mx + b is an explicit function –y is considered the dependent variable –x is the independent variable The equation.
Lecture 34 - Ordinary Differential Equations - BVP CVEN 302 November 28, 2001.
Parabolic PDEs Generally involve change of quantity in space and time Equivalent to our previous example - heat conduction.
FEM In Brief David Garmire, Course: EE693I UH Dept. of Electrical Engineering 4/20/2008.
1Chapter 2. 2 Example 3Chapter 2 4 EXAMPLE 5Chapter 2.
Chapter 2 Solution of Differential Equations
Scientific Computing Lab Organization Dr. Miriam Mehl Institut für Informatik Scientific Computing in Computer Science.
Introduction to Scientific Computing II Molecular Dynamics – Introduction Dr. Miriam Mehl Institut für Informatik Scientific Computing In Computer Science.
3.5 – Solving Systems of Equations in Three Variables.
Engineering Analysis – Computational Fluid Dynamics –
Implicit Differentiation. Implicitly vs. Explicitly Defined Functions y is given explicitly as a function of x (y is solved in terms of x) y is given.
3.7 – Implicit Differentiation An Implicit function is one where the variable “y” can not be easily solved for in terms of only “x”. Examples:
Introduction to Scientific Computing II Multigrid Dr. Miriam Mehl Institut für Informatik Scientific Computing In Computer Science.
Introduction to Scientific Computing II Multigrid Dr. Miriam Mehl.
Partial Derivatives Example: Find If solution: Partial Derivatives Example: Find If solution: gradient grad(u) = gradient.
Introduction to Scientific Computing II Molecular Dynamics – Algorithms Dr. Miriam Mehl Institut für Informatik Scientific Computing In Computer Science.
Scientific Computing Lab Outlook / State of Research Dr. Miriam Mehl Institut für Informatik Scientific Computing in Computer Science.
Scientific Computing Lab Results Worksheet 4 Dr. Miriam Mehl Institut für Informatik Scientific Computing in Computer Science.
Scientific Computing Lab Organization Dr. Miriam Mehl Institut für Informatik Scientific Computing in Computer Science.
Solving Partial Differential Equation Numerically Pertemuan 13 Matakuliah: S0262-Analisis Numerik Tahun: 2010.
1 LECTURE 6 Stability of Parabolic PDEs. 2 Aim of Lecture Last week we discussed Parabolic PDEs –Looked at Explicit and Implicit Methods –Advantages and.
Lecture Objectives: - Numerics. Finite Volume Method - Conservation of  for the finite volume w e w e l h n s P E W xx xx xx - Finite volume.
EEE 431 Computational Methods in Electrodynamics
Partial Differential Equations and Applied Mathematics Seminar
Chapter 30.
Chain Rules for Functions of Several Variables
Scientific Computing Lab
Scientific Computing Lab
CHAPTER 3 NUMERICAL METHODS.
A Few More LBM Boundary Conditions
Pressure Poisson Equation
Partial Differential Equations
Sec 5.5:Variation of Parameters
Scientific Computing Lab
Introduction to Scientific Computing II
finite element method node point based strong form
Introduction to Scientific Computing II
finite element method node point based strong form
Implicit Differentiation
Introduction to Scientific Computing II
Practical Course SC & V Discretization II AVS Dr. Miriam Mehl
Introduction to Scientific Computing II
Introduction to Scientific Computing II
Chapter 1:First order Partial Differential Equations
Scientific Computing Lab
Scientific Computing Lab
PARTIAL DIFFERENTIAL EQUATIONS
Introduction to Ordinary Differential Equations
Solving Equations with Variables on Both Sides
Solving Equations with Variables on Both Sides
Introduction to Scientific Computing II
Introduction to Scientific Computing II
Introduction to Scientific Computing II
Spatial Discretisation
TWO STEP EQUATIONS 1. SOLVE FOR X 2. DO THE ADDITION STEP FIRST
6th Lecture : Numerical Methods
MATH 2140 Numerical Methods
Balanced scales and equations
Chapter 1: First-Order Differential Equations
Presentation transcript:

Scientific Computing Lab Institut für Informatik Scientific Computing in Computer Science Scientific Computing Lab Partial Differential Equations Instationary Equations Dr. Miriam Mehl

Instationary PDEs independent variables: space + time boundary values + initial values boundaries + start and end

Discretization spatial discretization system matrix time discretization compare ODE

Balancing Accuracy time space relation time step spatial step

Explicit/Implicit explicit time steps restricted time steps dependend on spatial step no equations to solve implicit time step unrestricted time step large systems of equations

More Information http://www.cse.tum.de/vtc/SciComp/ 3.3 Discretizing partial Differential Equations

Peter Gustav Lejeune Dirichlet 1805-1859

Carl Gottfried Neumann 1832-1925