Migration Design Pattern Catalog (DPC) Context-Indexed Functor Migration Architecture

This document defines a systematic collection of migration design patterns for transforming traditional parameterized machine learning systems into context-indexed Functor Model Architectures (FcMA). The framework introduces explicit context localization through the context function c(x), localized parameter restriction, deterministic validation boundaries, and event-driven distributed execution. Rather than treating migration as a direct replacement of a stochastic model with a purely deterministic equivalent, this framework models migration as a localization process. Global parameter spaces and dense token contexts are decomposed into localized operational regions over an explicit base ontology. The resulting architecture supports energy reduction, auditability, horizontal scaling, and governed realization pipelines while preserving required inference semantics where applicable.