How Can I Perform a Double Integration of a Function with Given Data Sets?

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The discussion centers on performing a double integration of a function f(x(t1), y(t2)) over specified limits for t1 and t2, with datasets for x and y at evenly spaced intervals. The user seeks assistance with this integration process. They mention their experience with the Runge-Kutta method for solving differential equations, emphasizing the necessity of an initial state, such as position and velocity, particularly in applications like ballistics. This method involves iterative calculations to predict new positions and velocities, requiring a feedback loop for improved accuracy in initial predictions. The conversation highlights the challenges of integrating complex functions and the importance of initial conditions in numerical methods.
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I need to do a double integration of the form

int{ int{ f(x(t1),y(t2)) dt1 dt2 } } limit over t1 {0 : pa} and over t2 {o:pb}.

Now i have a data set for x for equal spaced t1 values varing from 0 to pa .
and for y for equal spaced t2 values varing from 0 to pb.

I will be very happy if anyone could help me ?
 
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There may be better methods but the only way I solved differential equations with software was via the Runge-Kutta method:

http://en.wikipedia.org/wiki/Runge-Kutta

The issue here is you need an initial state, for example for ballistics, you'd need the initial position and velocity. These would be used to calcuate accelerations, and the Runge Kutta method would be used to predict a new position and velocity after a very small period of time had passed. This process is repeated until the path is "completed".

For the first few steps, some type of feedback loop is needed to make the initial predictions more accurate.
 
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