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English(EN) Shared Symbolic Backbones for Physically Consistent Multi-Output Symbolic Regression

新方法提取多输出回归的共享符号骨干

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种新颖的神经进化符号回归方法，旨在处理耦合的多输出系统。该方法侧重于发现共享符号骨干，即一组可重用的潜在符号单元，然后通过稀疏读出将其适配到各个输出。该方法旨在强制执行和诊断跨输出一致性，特别是在物理参数共享且数据可识别性较弱时，提供一种结构化机制提取器，而非通用预测器。 AI

影响 该方法为提取复杂系统中的共享机制提供了一种专业化方法，有望提高科学建模的可解释性。

排序理由 该集群包含一篇详细介绍符号回归新方法的学术论文。

在 arXiv cs.AI 阅读 →

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

新方法提取多输出回归的共享符号骨干

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该集群包含一篇详细介绍符号回归新方法的学术论文。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Manuel Rodriguez ·

    用于物理一致性多输出符号回归的共享符号骨干

    arXiv:2607.26528v1 Announce Type: cross Abstract: Symbolic regression provides analytical expressions, but it is usually applied one output at a time. This is limiting in process systems, where state variables are often coupled through shared physical parameters. Independent symb…

  2. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Manuel Rodriguez ·

    用于物理一致性多输出符号回归的共享符号骨干

    Symbolic regression provides analytical expressions, but it is usually applied one output at a time. This is limiting in process systems, where state variables are often coupled through shared physical parameters. Independent symbolic regression can give accurate individual equat…

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