Is There a General Equation for the Stormer-Verlet Numerical Integration Method?

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In summary, the Stormer-Verlet integration method was specifically developed for solving Newton's equation F=ma or \ddot{x}=a(x(t)) in initial value problems. It can be represented by the recursive relationship x_{n+1}=2x_n -x_{n-1}+a_n\Delta t^2 + O(\Delta t^4) and is usually seen in this form. However, it can be extended to more general cases by transforming the equation and constructing the variable r(t) exactly. This method is not commonly used, but it is possible to deal with damped systems with a velocity term. The transformation used is a standard one for second order ODEs and can be found in many
  • #1
Master J
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Is there an explicit equation for the Stormer-Verlet numerical integration method for any problem?
I usually only see it in a formulation that is specific to a given problem. Is there a general equation?
 
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  • #2
Stormer-Verlet integration was developed specifically to solve Newtons equation F=ma, or:
[itex]\ddot{x}=a(x(t))[/itex] for initial value problems.
Stormer-Verlet integration leads to the relation:
[itex]x_{n+1}=2x_n -x_{n-1}+a_n\Delta t^2 + O(\Delta t^4)[/itex]

That's why you usually only see it in this form.

However, it is easy to extend this to more general cases, although I've never seen people do this. For the general second order ode:

[itex]a(t)\ddot{x}+b(t)\dot{x}+c(t)x=0[/itex],

first transform to get rid of the [itex]\dot{x}[/itex] term:

[itex]\ddot{y}=ry[/itex], with [itex]r=\frac{b(t)^2+2a(t)\dot{b}(t)-2b(t)\dot{a}(t)-4a(t)c(t)}{4a(t)^2}[/itex]

then, proceed as usual to get the recursive relationship:

[itex]y_{n+1}=2y_n -y_{n-1}+r_n\Delta t^2 + O(\Delta t^4)[/itex]

Note that to get the same accuracy as the original Stormer-Verlet method, you have to construct r(t) exactly first, you should not approximate the derivatives of a(t),b(t) and c(t).
By the way, when a,b,c are constants, r reduces to [itex]r=\frac{b^2-4ac}{4a^2}[/itex]

To get the first derivative (velocity), you need to do the usual (leapfrogging or central differencing)

For other differential equations, you need to switch to other methods though.
 
  • #3
hello,
i have the same problem. I thought Verlet's integration scheme is not practicable for damped systems, i.e. with a therm dependent on the velocity on the left side of the equation of motion. Could you please explain the transformation property a little more detailed? Or if you could give me some literature, where this transformation is discussed i yould be pleased...

Greetings from Austria
 
  • #4
I don't see why a velocity term would introduce problems. You even have the velocity-verlet algorithm that deals with exactly this problem, and this has the same order of accuracy, but maybe the region of stability is much smaller?

Anyway, the transformation above is a standard transformation for second order ODE's that can probably be found in many textbooks on ordinary differential equations. I don't know if it has a specific name. it can be found for instance on the Wolfram page:

http://mathworld.wolfram.com/Second-OrderOrdinaryDifferentialEquation.html

As I said above, if you construct r(t) directly from the ODE, then the problem reduces to the standard (Stormer-)Verlet algorithm.
 
  • #5


The Stormer-Verlet method is a numerical integration technique commonly used in solving differential equations in physics and engineering. It is a variation of the Verlet integration method and is known for its accuracy and stability.

In response to your question, there is not a general equation for the Stormer-Verlet method that can be applied to any problem. This is because the method is usually implemented in a specific formulation tailored to the problem at hand.

However, the general algorithm for the Stormer-Verlet method involves using a Taylor series expansion to approximate the position and velocity of a particle at a future time step. This is then used to iteratively calculate the position and velocity at each time step, taking into account the acceleration and forces acting on the particle.

Therefore, while there may not be a single explicit equation for the Stormer-Verlet method, its general algorithm can be applied to a wide range of problems. This makes it a versatile and widely used numerical integration technique in various fields of science and engineering.
 

What is the Stormer-Verlet method?

The Stormer-Verlet method is a numerical method used to solve ordinary differential equations (ODEs). It is a second-order accurate method that is commonly used in physics and engineering to model systems that exhibit oscillatory behavior.

How does the Stormer-Verlet method work?

The Stormer-Verlet method involves using a series of approximations to calculate the values of a function at different points in time. It uses a combination of the function's current position and velocity, as well as its position and velocity at the previous time step, to calculate the next position and velocity. This process is repeated until the desired accuracy is achieved.

What are the advantages of using the Stormer-Verlet method?

One of the main advantages of the Stormer-Verlet method is its second-order accuracy, which means that it can provide more accurate results compared to other numerical methods. It is also symplectic, meaning it conserves energy and momentum in the system being modeled. Additionally, it is relatively simple to implement and computationally efficient.

What types of problems can the Stormer-Verlet method be used for?

The Stormer-Verlet method is commonly used to solve problems involving oscillatory motion, such as the motion of a pendulum or a mass on a spring. It can also be used to simulate complex systems in physics and engineering, such as molecular dynamics or celestial mechanics.

Are there any limitations to the Stormer-Verlet method?

Like any numerical method, the Stormer-Verlet method has its limitations. It may not be suitable for all types of ODEs, particularly those that do not exhibit oscillatory behavior. It also requires a small time step to maintain accuracy, which can lead to longer computation times for larger systems.

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