sampen | Partial Python port of PhysioNet SampEn C code

It's a broad concept with several related terms.

A common, closely related term is Memoization, wherein the results of computing a subproblem for different inputs are stored, and reused when a previously-seen input is re-encountered. That's slightly different from what you're doing here, since memoization is a form of lazy evaluation where values are recognized opportunistically rather than the code performing an up-front exhaustive enumeration of the inputs which will be processed.

Materialization is also worth mentioning. It's encountered in the context of databases, and refers to the results of a query (a.k.a. tabular processing including possible filtering and/or reduction) being stored for reuse. The active concerns with materialization are largely around long-term considerations like dynamic updates, so it's not a perfect match for a run-and-forget algorithm.

Speaking of 'dynamic', one could also maybe describe this as a form of dynamic programming, with a problem solved by exhaustively enumerating and solving a sequence of subproblems. In dynamic programming, though, one expects those subproblems to have a more regular and inductive form, so I think that one's a stretch.

I would describe the precise strategy here as a sort of "eager memoization", to contrast with the lazy-evaluation assumption normally inherent with memoization.