Modern aircraft design has reached a level of complexity where multiphysics coupling – the interaction of aerodynamic, structural, thermal, electromagnetic, and other physical domains – must be considered to achieve optimal performance and reliability. Traditional model-driven approaches, which rely on fundamental physics-based models and equations, struggle to fully capture these complex coupled phenomena and are often limited by modeling assumptions and computational expense. At the same time, purely data-driven approaches using big data and machine learning have emerged as powerful tools to identify patterns and optimize designs, but they can lack physical interpretability and require extensive data. This paper provides a comprehensive review of the state-of-the-art methods for aircraft multiphysics coupling analysis and design optimization, bridging model-driven and data-driven paradigms. First, we introduce the concept of multiphysics coupling in aerospace engineering and discuss its inherent challenges and importance in modern design, citing industrial initiatives (e.g., digital engineering strategies and digital twin concepts) that underscore the need for integrated simulation. Next, we compare model-driven and data-driven approaches: model-driven methods (e.g., using computational fluid dynamics or finite element models) offer accuracy grounded in physics but cannot easily cover all aspects of highly complex systems, while data-driven methods (e.g., machine learning surrogates) excel at fitting complex relationships from data yet may sacrifice some accuracy or explanatory power. We highlight the historical transition from model-driven to data-driven techniques – for instance, the Monte Carlo method, first introduced during the Manhattan Project, enabled solving problems (like neutron diffusion) too complex for purely analytical models. Then, we examine modern hybrid approaches that integrate physics-based models with data-driven algorithms to leverage the strengths of both. Finally, we review optimization algorithms used for design in multiphysics contexts. Even with advanced models or data, effective design optimization often requires heuristic and evolutionary algorithms to navigate large design spaces with multiple conflicting objectives. We explain how heuristic algorithms can efficiently find “good enough” solutions without guaranteeing a global optimum, and how multi-objective optimization techniques (such as genetic algorithms and Pareto front analysis) are applied to balance trade-offs like weight vs. strength or efficiency vs. cost. This survey covers recent achievements in applying data-driven methods to complex coupled physics problems (e.g., using AI to predict structural and thermal behavior ), and discusses outstanding challenges such as ensuring model interpretability, improving computational efficiency, and managing sparse data. After reading this abstract, the reader should understand that this paper discusses why combining physics-based models with data-driven tools is essential for next-generation aircraft design, what methods exist to do so, and how optimization algorithms help achieve the best designs under multiphysics constraints.
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