Heuristic Learning for Active Flow Control Using Coding Agents

Active flow control involves nonlinear dynamics, partial observations, and computationally expensive simulations, making controller design particularly challenging. Deep reinforcement learning (DRL) has emerged as a powerful framework for such problems, but its success typically relies on large numbers of simulator interactions and produces neural-network policies whose decision process often remains difficult to interpret. In this work, we investigate a different paradigm: instead of optimizing

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ethics.ai (13 July 2026), “Heuristic Learning for Active Flow Control Using Coding Agents,” evidence record 3046, https://ethics.ai/record/3046 (originally published by arXiv cs.AI).

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