English(EN) DASyR-LLM: Domain-Aware Symbolic Regression with LLMs for Kinetic Model Discovery

LLM指导的符号回归加速动力学模型发现

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-05 17:48

一个名为DASyR-LLM的新框架将大型语言模型（LLM）与符号回归相结合，以加速化学工程中动力学模型的发现。LLM组件提供领域特定的模型评估和新表达式建议，显著减少了识别准确速率表达式所需的迭代次数。该方法在各种案例研究中显示迭代次数减少了41.7%-79.3%，可能为实验工作带来可观的节省。 AI

影响通过减少动力学模型开发中的实验迭代来加速科学发现。

排序理由介绍科学模型发现新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 Hugging Face Daily Papers 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 介绍科学模型发现新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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Hugging Face Daily Papers TIER_1 English(EN) · 2026-08-05 17:48

DASyR-LLM：基于LLM的领域感知符号回归用于动力学模型发现

Kinetic model discovery is a central challenge in chemical engineering, as accurate rate expressions are essential for understanding and controlling chemical and biological processes. Symbolic regression (SR) has emerged as a powerful data-driven approach for identifying interpre…

本文如何被排名

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DASyR-LLM：基于LLM的领域感知符号回归用于动力学模型发现

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