Spatial discretization of high-dimensional partial differential equations requires data representations
that are of low overhead in terms of memory and complexity. Uniform discretization of computational
domains quickly grows out of reach due to an exponential increase in problem size with dimensionality.
Even with spatial adaptivity, the number of mesh data points can be unnecessarily large if care is not
taken as to where refinement is done. This paper proposes an adaptive scheme that generates the mesh
by recursive bisection, allowing mesh blocks to be arbitrarily anisotropic to allow for fine structures in
some directions without over-refining in those directions that suffice with less refinement. Within this
framework, the mesh blocks are organized in a linear kd-tree with an explicit node index map corresponding
to the hierarchical splitting of internal nodes. Algorithms for refinement, coarsening and 2:1
balancing of a mesh hierarchy are derived. To demonstrate the capabilities of the framework, examples
of generated meshes are presented and the algorithmic scalability is evaluated on a suite of test problems.
In conclusion, although the worst-case complexity of sorting the nodes and building the node
map index is n2, the average runtime scaling in the studied examples is no worse than n log n.
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