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English(EN) Systematic LLM Translation of Legacy Scientific Code to Differentiable Frameworks: Application to a Land Surface Model

LLM 管道将遗留 Fortran 代码翻译成 JAX 以用于科学建模

研究人员开发了一个新颖的五阶段管道,利用基于 LLM 的代理将遗留 Fortran 代码翻译成 JAX 框架。该系统自动化了代码迁移过程,包括依赖分析、自主纠错和数值奇偶校验。该管道成功应用于 CLM-ml-v2,一个 19,000 行的陆面模型,生成了一个可微分版本,显著加快了计算和参数恢复。 AI

影响 能够快速区分复杂的科学模型,加速研究和参数估计。

排序理由 该集群包含一篇详细介绍新方法及其应用的学术论文。

在 arXiv cs.MA (Multiagent) 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

LLM 管道将遗留 Fortran 代码翻译成 JAX 以用于科学建模

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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Aya Lahlou, Linnia Hawkins, Pierre Gentine ·

    系统性地将遗留科学代码翻译成可微分框架:以陆面模型为例

    arXiv:2606.07681v1 Announce Type: cross Abstract: Differentiable programming offers transformative capabilities for scientific modeling, enabling gradient-based parameter estimation, sensitivity analysis, and data assimilation. Yet, migrating legacy codebases into differentiable …

  2. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Pierre Gentine ·

    系统性地将遗留科学代码翻译成可微分框架:以陆面模型为例

    Differentiable programming offers transformative capabilities for scientific modeling, enabling gradient-based parameter estimation, sensitivity analysis, and data assimilation. Yet, migrating legacy codebases into differentiable frameworks remains a challenge. We present a five-…