Computational Physics Differential Equations
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Transcript of Computational Physics Differential Equations
Dr. Guy Tel-Zur
Computational PhysicsDifferential Equations
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Agenda• MHJ Chapter 13 & Koonin Chapter 2• How to solve ODE using Matlab• Scilab
Topics• Defining the scope of the discussion• Simple methods• Multi-Step methods• Runge-Kutta• Case Studies - Pendulum
The scope of the discussion
For a higher order ODE a set of coupled 1st order equations:
Simple methodsEuler method:
Integration using higher order accuracy:
Taylor series expansion:
Local error!
Better than Euler’s method but useful only when it is easy to differentiate f(x,y)
ExampleLet’s solve:
FORTRAN code
Multi-Step methods
-1-1
Adams-Bashforth2 steps:
4 steps:
(So far) Explicit methodsFuture = Function(Present && Past)
Implicit methods
Future = Function(Future && Present && Past)
Let’s calculate dy/dx at a mid way between lattice points:
Rearrange:
This is a recursion relation!
Let’s replace:
A simplification occurs if f(x,y)=y*g(x), then the recursion equation becomes:
An example, suppose g(x)=-x
yn+1=(1-xnh/2)/(1+xn+1h/2)yn
This can be easily calculated, for example:
Calculate y(x=1) for h=0.5
X0=0, y(0)=1X1=0.5, y(0.5)=?x2=1.0, y(1.0)=?
Error=-0.01569
The solution:
Predictor-Corrector method
Predictor-Corrector method
Runge-Kutta
Proceed to:Physics examples: Ideal harmonic oscillator – section 13.6.1
Physics Project – The pendulum, 13.7
I use a modified the C++ code from:http://www.fys.uio.no/compphys/cp/programs/FYS3150/chapter13/cpp/program2.cpp
fout.close fout.close()
Demo on folder:C:\Users\telzur\Documents\BGU\Teaching\ComputationalPhysics\2011A\Lectures\05\CPP
ODEs in Matlabfunction dydt = odefun(t,y)a=0.001;b=1.0;dydt -=b*t*sin(t)+a*t*t;
Usage:
[t1, y1]=ode23(@odefun,[0 100],0);Plot(t1,y1,’r’);hold onplot(t2,y2,’b’);hold off
Demo folder: Users\telzur\Documents\BGU\Teaching\ComputationalPhysics\2011A\Lectures\05\Matlab
Output
http://www.scilab.org
Parallel tools for Multi-Core and Distributed Parallel Computing
In preparationParallel executionA new function (parallel_run) allows parallel computations and leverages multicore architectures and their capacities.
Xcos demo