Symplectic integrator/hamiltonian

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Discussion Overview

The discussion revolves around finding a numerical solution for a Hamiltonian mechanics problem using symplectic integrators. Participants explore the methods and resources available for solving nonlinear differential equations, particularly in chaotic systems, and share their preferences for numerical techniques.

Discussion Character

  • Exploratory
  • Technical explanation
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • One participant seeks resources on symplectic integrators for Hamiltonian mechanics problems, indicating a lack of coverage in their computational physics textbook.
  • Another participant references Wikipedia and suggests that the equations must be represented in a specific way, proposing diagonalization as a preferred method, although they note it may not be the fastest approach.
  • A different participant expresses the need for more detailed information, highlighting the complexity of their specific Hamiltonian system, which involves messy nonlinear differential equations that are likely non-separable.
  • Another participant argues that the chaotic nature of the system does not significantly affect numerical integration but emphasizes the importance of selecting a good method. They recommend testing with systems that have analytical solutions and using existing libraries for numerical integration.
  • This participant also suggests looking for software that provides error estimations and considering stability with respect to time-step adjustments and adaptive methods.

Areas of Agreement / Disagreement

Participants do not reach a consensus on the best approach or resources for solving the Hamiltonian mechanics problem, indicating multiple competing views and unresolved questions regarding the methods and their applicability.

Contextual Notes

Participants mention various methods and resources without agreeing on specific solutions or approaches. There are unresolved aspects regarding the representation of equations and the choice of numerical methods, particularly in relation to chaotic systems.

Who May Find This Useful

Individuals interested in numerical methods for Hamiltonian mechanics, particularly those dealing with chaotic systems and nonlinear differential equations, may find this discussion relevant.

nlake27
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I'm trying to get a numerical solution for a hamiltonian mechanics problem. According to wikipedia, there's a method of solving the resulting differential equations called a symplectic integrator that's designed specifically for such problems, but my computational physics textbook doesn't mention it. If anyone could point me in the direction of a good basic resource (website, text, whatever), I'd be grateful. Thanks.
 
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Have you read this on wiki: http://en.wikipedia.org/wiki/Symplectic_integrator
It is rather clear that you have to represent equation (2) or (3) in some way.
This will end-up, I think, to solving linear systems of equations on short time steps.
In other words, any method will do it.
My preffered is based on diagonalisation, just as in QM. It may not be the fastest.
What I remember the best from QM is how to compute practically the exponential of an operator (by diagonalisation).
 
Yes, I've read this; I'm looking for something more detailed. The Hamiltonian in question yields a dimension-2 system of messy nonlinear differential equations that are, I think, non-separable. The numerical solutions need to be pretty precise because it's a chaotic system.
 
Numerically, I think, it doesn't make a difference if it is a chaotic system or not.
But you need to choose a good method, of course.
I suggest you to try first by integrating some other system with possibly an analytical solution to check for errors.
I also suggest you to use existing librairies: IMSL, NAG, see netlib, and many othes, look also for matlab
Try to find a software that provides error estimations, possibly.
If not possible, try to look at the statbility of your solution with respect to the time-step or other parameters used for improving the precision.
Consider adaptative method and implicit methods.

Keywords: numerical integration of differential equations
 

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