Technical Explanation of Level Set Methods
Given a moving closed hypersurface G(t),
we wish to produce an Eulerian
formulation for the motion of the hypersurface propagating along its
normal direction with speed F, where F can be a function of
various arguments, including the curvature, normal direction, etc.
The main idea is to embed this propagating interface as the
zero level set of a higher dimensional function phi.
Let phi(x,t=0), where x is in ndimensional space, be defined by
phi(x,t=0) = d(signed)
where d(signed) is the distance from x to G(t=0), and
the plus (minus) sign is chosen if the point x is outside (inside)
the initial hypersurface G(t=0).
Thus, we have an initial function phi(x,t=0)
with the property that
G(t=0) = ( x  phi( x, t= 0) = 0 )
Our goal is to now produce an equation for the evolving function
phi(x,t) which contains the embedded motion of
G(t) as the level set phi = 0.
Let x(t) be the path of a point on the
propagating front.
That is, x (t=0) is a point on the initial front G(t=0),
and dx/dt = F (x(t)) with the vector
dx/dx normal to the front at x(t).
Since the evolving function phi is always zero on the
propagating hypersurface, we must have
phi( x ( t) , t ) = 0
By the chain rule,
d(phi)/dt + grad ( x(t,t) ) * dx/dt= 0
where grad is the gradient operator, and the * denotes the dot product.
Since F already gives the speed in the outward normal direction,
then dx/dt * n = F, where n = grad phi /grad phi.
Thus, we then have the evolution equation for phi(x,t), namely
d(phi)/dt + F  grad phi  = 0
with
phi (x,t=0 ) given
We refer to this as a HamiltonJacobi ``type'' equation because, for
certain forms of the speed function F, we obtain the standard
HamiltonJacobi equation.
There are four major advantages to this Eulerian HamiltonJacobi formulation.

First,the evolving function phi(x,t) always remains
a function as long as F is smooth.
However, the level surface phi = 0, and hence the propagating
hypersurface G(t), may change topology, break, merge, and form
sharp corners as the function phi evolves.

The second advantage of this Eulerian formulation concerns numerical
approximation.
Because phi(x,t) remains a function as it evolves, we may
use a discrete grid in the domain of x and substitute finite
difference approximations for the spatial and temporal derivatives.
For example, using a uniform mesh of spacing h on an orthogonal
grid, with grid nodes (i,j), and
employing the standard notation that phi(i,j,n)
is the approximation to the solution phi(i h , j h , n k), where
k is the time step,
we might write
(phi(i,j,n+1) phi(i,j,n))/k +
( F ) ( grad(i,j) phi(i,j,n))
= 0
where we have used forward differences in time, and
grad(ij) phi(i,j,n) represents some appropriate finite difference
operator for the spatial derivative.
As discussed above, the correct entropysatisfying approximation to the
difference operator comes from exploiting the technology of hyperbolic
conservation laws. Following (Sethian, J.A.. Level Set Methods, Cambridge
University Press, 1996), given a speed
function F(K), we update the front by the
following scheme. First, separate F(K) into a constant advection term F0
and the remainder F1 (K), that is,
F(K) = F0 + F1 (K)
The advection component F0 of the speed function is then approximated
using upwind schemes, while the remainder is approximated using central
differences. In one space dimension with positive F0, we have
phi(i,n+1) = phi(i,n)  (k) (F0)
sqrt [max (DM phi(i),0)^2 + min (DP phi(i),0)^2 ]
  F1(K) grad phi(i,n) 
where DM and DP are the backwards and forwards difference operators.
Extension to higher dimensions are straightforward.

The third major advantage of the above formulation is that intrinsic
geometric properties of the front may be easily determined from the
level function phi. Both the normal derivative and the curvature
may be easily calculated by finite difference approximations.

Finally, the fourth major advantage of the above level set approach is that
there are no significant differences in following fronts in three
space dimensions.
By simply extending the array structures and gradients operators,
propagating surfaces are easily handled.

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