write some docs

Author: sjkelly

docs/src/index.md

@@ -1,5 +1,50 @@
 # DistMesh.jl
 
+
+DistMesh.jl implements simplex refinement on signed distance functions, or anything that
+has a sign, distance, and called like a function. The algorithm was first presented
+in 2004 by Per-Olof Persson, and was initially a port of the corresponding Matlab Code.
+
+## What is Simplex Refinement?
+
+In layman's terms, a simplex is either a triangle in the 2D case, or a tetrahedra in the 3D case.
+
+When simulating, you other want a few things from a mesh of simplices:
+    - Accurate approximation of boundaries and features
+    - Adaptive mesh sizes to improve accuracy
+    - Near-Regular Simplicies
+
+DistMesh is designed to address the above.
+
+## Algorithm Overview
+
+The basic processes is as follows:
+
+
+
+## Comparison to other refinements
+
+DistMesh generally has a very low memory footprint, and can refine without additional
+memory allocation. Similarly, since the global state of simplex qualities is accounted for
+in each refinement iteration, this leads to very high quality meshes.
+
+Aside from the above, since DistMesh works on signed distance functions it can handle
+complex and varied input data that are not in the form of surface meshes (Piecewise Linear Complicies).
+
+## Difference from the MatLab implementation
+
+Given the same parameters, the Julia implementation of DistMesh will generally perform
+4-60 times faster than the MatLab implementation. Delaunay Triangulation in MatLab uses
+QHull, whereas DistMesh.jl uses TetGen.
+
+## How do I get a Signed Distance Function?
+
+Here are some libraries that turn gridded and level set data into an approximate signed
+distance function:
+
+- Interpolations.jl
+- AdaptiveDistanceFields.jl
+
 ```@index
 ```