Game Development Reference
In-Depth Information
The thing is, basic (forward) Euler integration has a hard time dealing with stiff
springs. This is the major drawback of forward Euler integration, and often a
showstopper. The problem is that particle positions and velocities come out of
sync when time steps are too large. This in turn leads to instabilities, which lead
to pain and suffering.
11.2.2 Backward Euler Integration
A way to make up for this is to use implicit integration. The method of back-
ward Euler integration belongs to the family of implicit-integration methods. The
members of this family all provide more stability in situations with stiff equations
and generally let us use larger time steps without the risk of the system blowing
up.
With backward Euler integration, we update the current position, not with
the current velocity and acceleration vectors (as was the case with basic Euler
integration) but with the resulting velocity vector v ( t t ) and the resulting ac-
celeration vector a ( t t ). The problem with this approach, however, is that the
acceleration and velocity at time t t are unknown, and therein lies the prob-
lem with backward Euler integration: as we cannot directly evaluate the update
velocity and acceleration, we need to solve for the unknowns. The resulting set
of equations can be rather large if there are many particles. This calls for (usually
slow) numerical methods for solving equations. This means that in their basic
forms, neither backward nor forward Euler integration is immediately useful for
our purpose.
11.2.3 Other Approaches
Experimentingwith other approaches, such as adaptive integration or higher-order
integration methods such as Runge-Kutta, may bring you closer to the desired
result, but these methods, too, are not ideal choices for real-time, interactive use
for the same reasons: they are basically either slow or unstable. So what does an
intelligent game physics programmer do? It seems we cannot escape either having
to deal with instability or too much elasticity in the case of explicit integration
methods or being forced to solve unwieldy systems of equations in the case of
implicit integration methods—or, alternatively, waiting an eternity for adaptive
methods to finish.
Luckily, as it turns out, we can have the best of two worlds. The so-called
semi-implicit methods (also known as semi-explicit methods) are both simple and
stable. And while we may lose some accuracy in some cases, it doesn't really
matter in the case of game simulation. Who cares if the dead body flies ten percent
too far or too short? We're not sending a (real) rocket to the moon. On the other
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