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AI agents autonomously discover and train turbulence models using differentiable CFD

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel gradient-aware AI agent framework that autonomously discovers and trains turbulence models for computational fluid dynamics (CFD). This system utilizes a multi-agent large language model (LLM) to search for closure forms, while a differentiable CFD solver provides the necessary gradients for training. The agents can construct differentiable training programs, embed various closure types, and decide on optimization strategies, enabling end-to-end autonomous development of turbulence models. Demonstrations include discovering closures for 2D turbulence and training wall models for 3D channel flow simulations, showing improved agreement with target statistics compared to gradient-free methods. AI

IMPACT This research demonstrates a new paradigm for AI-driven scientific discovery, potentially accelerating advancements in fields like fluid dynamics and beyond.

RANK_REASON Academic paper detailing a new AI methodology for scientific discovery. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.MA (Multiagent) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI agents autonomously discover and train turbulence models using differentiable CFD

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Academic paper detailing a new AI methodology for scientific discovery. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Jian-Xun Wang ·

    Gradient-Aware AI Agents for Autonomous Turbulence Model Discovery via Differentiable CFD

    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…

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