This is the model-structured / neural-compositional-module idea from the physics-informed-ML school, distilled to its interpretable core. The output is f(x) = Σᵢ φᵢ(x)·(aᵢx + bᵢ): local linear models blended by memberships φᵢ that form a partition of unity, so the model smoothly hands off from one local line to the next and the pieces always sum with total weight one. Two consequences matter. First, it is linear in the parameters (aᵢ, bᵢ), so training is ordinary least squares, deterministic, fast, no initialization or local-minima worries, and it recovers a linear target exactly. Second, fitting each local model as an independent membership-weighted regression makes its slope the true local derivative, you can read the model's behavior region by region, initialize it from physical knowledge, and audit it, none of which a black box permits. The full model-structured toolkit adds neural FIR blocks over past inputs to capture dynamics and uses physics-based (for example, exponential impulse-response) initialization; the payoff throughout is the same: comparable or better accuracy than a black box with far fewer parameters and far less data, plus interpretability. For physical systems, where data is scarce and trust is required, that trade is often exactly right.