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Fluid-dynamic drag accounts for a substantial fraction of energy consumption across air, ground, and maritime transport systems, making its reduction a critical lever for decarbonizing mobility. While active flow control (AFC) strategies have demonstrated significant drag reduction potential, their design remains constrained by heuristic physical assumptions about dominant flow structures. Recent developments in deep reinforcement learning (DRL) have emerged as a transformative paradigm, capable of autonomously discovering control strategies in high-dimensional turbulent environments. This perspective traces the evolution of drag reduction approaches from classical passive and active control approaches toward data-driven methods based on DRL. A particularly promising direction is the integration of explainable artificial intelligence (XAI) with DRL, which provides physically interpretable information about flow regions associated with drag generation and guides the learning process toward physically meaningful actuation schemes. As a result, XAI-guided DRL controllers have been shown in canonical configurations to achieve comparable or improved drag reduction with substantially lower actuation power than controllers trained directly for drag minimization. This transition from opaque optimization toward flow control informed by dynamical causal relationships represents a key step for the development of energy-efficient and sustainable flow-control solutions for transport systems.
Reverte-Badillo et al. (Tue,) studied this question.