Design-Space Coverage for Auxetic Lattice Surrogates

This evidence synthesis examines coverage measurement in parameterized re-entrant cells. It argues that the appropriate object of evaluation is the digital thread connecting geometry, materials, boundary conditions, solver settings, derived features, and predicted capacity, not a model score in isolation. The review brings together the assigned studies with established work on uncertainty, robustness, provenance, and governance. Across these literatures, a common problem emerges: a fast surrogate can move a design decision far outside the domain where its training simulations were physically credible. The proposed framework separates evidence quality, model behavior, decision policy, and operational monitoring, then asks how each layer changes under distribution shift, adversarial pressure, or incomplete information. It recommends evaluation by slices and repeated trials, explicit reject and escalation policies, preservation of data and reasoning lineage, and prospective monitoring tied to defined actions. The result is a research agenda for systems that are efficient enough to use but also bounded enough to audit. No new experiment is claimed; the article develops a comparative conceptual model and identifies tests that would make future empirical claims more credible.

1. Ma, Mingjun, et al. "MuSK: Multi-Scale Knowledge Learning for Provenance-Graph Anomaly Detection." *Computer Networks* 289 (2026): 112728.
2. Li, Yuanhao, et al. "BoostAPR: Boosting Automated Program Repair via Execution-Grounded Reinforcement Learning with Dual Reward Models." *arXiv preprint arXiv:2605.09134* (2026).
3. Wang, Lizhe, et al. "Synergizing Machine Learning and Multiscale Shakedown Method for Shakedown Loading Capacity Evaluation of Parameterized Lattice Structures." *Extreme Mechanics Letters* 75 (2025): 102297.
4. Saltelli, Andrea, et al. "Variance Based Sensitivity Analysis of Model Output: Design and Estimator for the Total Sensitivity Index." *Computer Physics Communications*, vol. 181, no. 2, 2010, pp. 259-270.
5. Breiman, Leo. "Random Forests." *Machine Learning*, vol. 45, 2001, pp. 5-32.
6. Friedman, Jerome H. "Greedy Function Approximation: A Gradient Boosting Machine." *Annals of Statistics*, vol. 29, no. 5, 2001, pp. 1189-1232.
7. Lundberg, Scott M., and Su-In Lee. "A Unified Approach to Interpreting Model Predictions." *Advances in Neural Information Processing Systems*, vol. 30, 2017.
8. Bendsoe, Martin P., and Ole Sigmund. *Topology Optimization: Theory, Methods, and Applications*. Springer, 2003.
9. Wilkinson, Mark D., et al. "The FAIR Guiding Principles for Scientific Data Management and Stewardship." *Scientific Data*, vol. 3, 2016, article 160018.
10. Suresh, S. *Fatigue of Materials*. 2nd ed., Cambridge University Press, 1998.
11. Miner, M. A. "Cumulative Damage in Fatigue." *Journal of Applied Mechanics*, vol. 12, 1945, pp. A159-A164.
12. Paris, P., and F. Erdogan. "A Critical Analysis of Crack Propagation Laws." *Journal of Basic Engineering*, vol. 85, no. 4, 1963, pp. 528-533.
13. Fatemi, Ali, and Darrell F. Socie. "A Critical Plane Approach to Multiaxial Fatigue Damage Including Out-of-Phase Loading." *Fatigue & Fracture of Engineering Materials & Structures*, vol. 11, no. 3, 1988, pp. 149-165.