This article proposes a three-step strategy for improving fuzzy systems using simple models and high-fidelity data. In the first step, data are generated from a basic model (e.g., CART tree), simplified, and converted into fuzzy sets using a GMM that defines membership functions. The process is a generic, open process. Then, in the second step, a refinement with high-precision data leads to a neutrosophic extension: new points are fed into a high-resolution model; for each rule, the degrees of truth (T), falsehood (F), and indeterminacy (I) are calculated. Finally, the last step is sending feedback to the initial model: the refined rules are reinterpreted and the fuzzy set is regenerated and merged again with the knowledge acquired in step 2, obtaining an updated FIS that preserves the computational lightness of step 1. Overall, the strategy combines automatic rule generation, neutrosophic refinement, and iterative reuse to improve accuracy without losing efficiency. The originality of this paper is that it closes the loop. There are studies on the first two steps but here we have included refinement and feedback by means of the neutrosophic paradigm. This work also outlines a numerical procedure for analyzing neutrosophic refinement convergence through a so-called Indeterminacy-First Aggregation Operator (IFAO). As a test case, we applied the proposed scheme to a Physics-Guided Unification of Scattering Regimes for Fractal Aggregates. The example was considered due to its clear physical regimes, explicit mathematical forms, and hierarchical structure, which provide an ideal testbed where we can demonstrate how to i) extract rules (the distinct power laws and Guinier law from data); ii) assemble them into a unified model; and iii) refine that model with a full physical theory.