@Article{cmes.2026.079830,
AUTHOR = {Zelong Liang, Tinh Quoc Bui, Zhichao Dong, Weihua Li, Yingjun Wang},
TITLE = {A Review on Emerging Unified Information–Physics Frameworks for Structural Design: Toward Topology Optimization Informatics},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {147},
YEAR = {2026},
NUMBER = {1},
PAGES = {0--0},
URL = {http://www.techscience.com/CMES/v147n1/67149},
ISSN = {1526-1506},
ABSTRACT = {Topology optimization (TO) has become a core computational paradigm for structural design by defining optimality through physics-based objectives and constraints. However, practical engineering design often involves incomplete and imperfect physical modeling due to multi-physics coupling, manufacturing uncertainty, and computational constraints, leaving critical design factors insufficiently captured in purely physics-driven formulations. In parallel, data-driven and generative methods have enabled rapid topology generation and intent-aware design exploration, yet often weaken explicit optimality guarantees. This review argues that these seemingly divergent developments can be organized under a unified information–physics perspective. We term this emerging field Topology Optimization Informatics (TOI): optimal structural design is obtained through the joint modeling and optimization of physical laws and design-relevant information. We first summarize the integration of artificial intelligence (AI) and TO into two major paradigms: AI-based one-shot TO, which learns mappings or distributions of near-optimal designs from data and prioritizes fast generation and diversity, and AI-enhanced iterative TO, which embeds learning-based modules into the classical solver-in-the-loop pipeline while keeping the underlying governing equations unchanged. Finally, we show that traditionally separate tasks—design control, computational acceleration, and fidelity enhancement—can be interpreted as different manifestations of information–physics co-modeling within a single optimization framework, thereby clarifying their connections and design implications and outlining opportunities for semantic- and data-enabled next-generation structural design.},
DOI = {10.32604/cmes.2026.079830}
}