English(EN) Gradient-Aware AI Agents for Autonomous Turbulence Model Discovery via Differentiable CFD

AI代理通过可微分CFD自主发现和训练湍流模型

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-11 22:50

研究人员开发了一种新颖的梯度感知AI代理框架，该框架可自主发现和训练计算流体动力学（CFD）的湍流模型。该系统利用多代理大型语言模型（LLM）搜索封闭形式，而可微分CFD求解器提供训练所需的梯度。代理可以构建可微分训练程序，嵌入各种封闭类型，并决定优化策略，从而实现湍流模型的端到端自主开发。演示包括发现二维湍流的封闭形式以及训练三维通道流模拟的壁面模型，与无梯度方法相比，显示出与目标统计数据的一致性有所提高。 AI

影响这项研究展示了一种由AI驱动的科学发现新范式，有可能加速流体动力学及其他领域的进步。

排序理由详细介绍AI驱动科学发现新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.MA (Multiagent) 阅读 →

computational fluid dynamics

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arXiv cs.MA (Multiagent) TIER_1 English(EN) · Jian-Xun Wang · 2026-11-11 22:50

用于通过可微分CFD进行自主湍流模型发现的梯度感知AI代理

Turbulence modeling relies on human choices of closure form, physical inputs, and tunable parameters. Training closures a posteriori is efficient only with gradients through long chaotic rollouts, which few solvers provide. Most a posteriori approaches therefore resort to gradien…

本文如何被排名

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用于通过可微分CFD进行自主湍流模型发现的梯度感知AI代理

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