Adds README overview to radiuss_tutorial

src/examples/CMakeLists.txt

@@ -82,7 +82,7 @@ install(
         examples/axom/radiuss_tutorial
 )
 
-foreach(_lesson lesson_00 lesson_01 lesson_02 lesson_03 lesson_04 patch)
+foreach(_lesson lesson_00 lesson_01 lesson_02 lesson_03 lesson_04 patch figs)
     install(DIRECTORY radiuss_tutorial/${_lesson}
             DESTINATION examples/axom/radiuss_tutorial)
 endforeach()

src/examples/radiuss_tutorial/README.md

@@ -0,0 +1,16 @@
+## Axom tutorial
+
+This tutorial showcases several features of Axom by incrementally building up an application that checks for self-intersections and degeneracies among the triangles in a triangle mesh. The resulting application matches functionality from Axom's [mesh_tester utility](https://github.com/LLNL/axom/blob/develop/src/tools/mesh_tester.cpp).
+
+
+![Mesh with self-intersections](figs/porsche.png)
+<div style="text-align: center;  font-style: italic;">
+A triangle mesh of a car with intersecting triangles highlighted in red
+</div>
+<br />
+
+* [Lesson 00](lesson_00/README.md) provides a brief overview of Axom and develops a simple application against an installed version of Axom
+* [Lesson 01](lesson_01/README.md) uses Axom to load an [STL triangle mesh](https://en.wikipedia.org/wiki/STL_(file_format)) and print out the number of triangles in the mesh. It uses Axom's logging component `slic` as well as functionality from its spatial querying component, `quest`.
+* [Lesson 02](lesson_02/README.md) adds a naive algorithm that checks every pair of triangles in the mesh for self-intersections, showcasing computational geometry primitives and operations from Axom's `primal` component.
+* [Lesson 03](lesson_03/README.md) ports this naive algorithm to [RAJA](https://github.com/llnl/raja) for performance-portable execution on available computing resources, using sequential (CPU), threaded (OpenMP) and GPU devices using `cuda` and `hip` backends.
+* [Lesson 04](lesson_04/README.md) uses a spatial index from Axom's `spin` component to further accelerate our self-intersection algorithm.