Meta-Path Reasoning Across Material, Geometry, and Load

Abstract

Which paths through design variables and stress fields correspond to plausible fatigue mechanisms? This review answers by treating heterogeneous causal paths as a property of a sociotechnical workflow rather than a feature that can be read from average accuracy. The focal setting is auxetic lattice design, where a fast surrogate can move a design decision far outside the domain where its training simulations were physically credible. Evidence from the assigned publications is synthesized with foundational studies of calibration, distribution shift, causal structure, and responsible deployment. Four requirements follow: preserve the lineage of parameterized geometries, finite-element and multiscale outputs, loading histories, material properties, and validation measurements; measure stability across relevant perturbations; connect confidence to a specific action; and maintain a route for human challenge and correction. The framework distinguishes descriptive performance from decision utility and separates uncertainty about the world from uncertainty created by the model and its evaluator. It also shows why faster inference or richer reasoning is valuable only when it improves a defined decision under a transparent resource budget. The article is a literature review and research agenda, not a report of a newly completed trial.