{
  "id": 3046,
  "url": "https://arxiv.org/abs/2607.11565v1",
  "title": "Heuristic Learning for Active Flow Control Using Coding Agents",
  "summary": "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",
  "authors": "Paul Garnier, Jonathan Viquerat, Elie Hachem",
  "category": "research",
  "topics": "agents-autonomy,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-13T13:47:17.000Z",
  "fetched_at": "2026-07-14T16:11:46.979Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/3046",
  "original_url": "https://arxiv.org/abs/2607.11565v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}