skeleton animation - Page 103

Runge-Kutta (RK4) integration for game physics

- by Kai

Gaffer on Games has a great article about using RK4 integration for better game physics. The implementation is straightforward but the math behind it confuses me. I understand derivatives and integrals on a conceptual level but I haven't manipulated equations in a long time. Here's the brunt of Gaffer's implementation: void integrate(State &state, float t, float dt) { Derivative a = evaluate(state, t, 0.0f, Derivative()); Derivative b = evaluate(state, t+dt*0.5f, dt*0.5f, a); Derivative c = evaluate(state, t+dt*0.5f, dt*0.5f, b); Derivative d = evaluate(state, t+dt, dt, c); const float dxdt = 1.0f/6.0f * (a.dx + 2.0f*(b.dx + c.dx) + d.dx); const float dvdt = 1.0f/6.0f * (a.dv + 2.0f*(b.dv + c.dv) + d.dv) state.x = state.x + dxdt * dt; state.v = state.v + dvdt * dt; } Can anybody explain in simple terms how RK4 works? Specifically, why are we averaging the derivatives at 0.0f, 0.5f, 0.5f, and 1.0f? How is averaging derivatives up to the 4th order different from doing a simple euler integration with a smaller timestep? After reading the accepted answer below, and several other articles, I have a grasp on how RK4 works. To answer my own questions: Can anybody explain in simple terms how RK4 works? RK4 takes advantage of the fact that we can get a much better approximation of a function if we use its higher-order derivatives rather than just the first or second derivative. That's why the Taylor series converges much faster than Euler approximations. (take a look at the animation on the right side of that page) Specifically, why are we averaging the derivatives at 0.0f, 0.5f, 0.5f, and 1.0f? The Runge-Kutta method is an approximation of a function that samples derivatives of several points within a timestep, unlike the Taylor series which only samples derivatives of a single point. After sampling these derivatives we need to know how to weigh each sample to get the closest approximation possible. An easy way to do this is to pick constants that coincide with the Taylor series, which is how the constants of a Runge-Kutta equation are determined. This article made it clearer for me: http://web.mit.edu/10.001/Web/Course%5FNotes/Differential%5FEquations%5FNotes/node5.html. Notice how (15) is the Taylor series expansion while (17) is the Runge-Kutta derivation. How is averaging derivatives up to the 4th order different from doing a simple euler integration with a smaller timestep? Mathematically it converges much faster than doing many Euler approximations. Of course, with enough Euler approximations we can gain equal accuracy to RK4, but the computational power needed doesn't justify using Euler.

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