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English(EN) LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks

新研究通过学习初始化和LLM指导设计来解决PINN训练失败问题

作者 PulseAugur 编辑部 · [3 个来源] ·

两篇新研究论文探讨了改进物理信息神经网络（PINN）的训练和设计方法。第一篇论文介绍了LIGO-PINN，一个使用学习初始化来克服PINN收敛失败的框架，在各种PDE领域展示了显著的性能提升。第二篇论文提出了一种进化算法来指导大型语言模型设计PINN，创建完整的、可执行的配置，这些配置会随着代际积累经验，并在波动方程上显示出改进的性能。 AI

影响 这些方法可以提高PINN在科学建模和模拟中的可靠性和效率。

排序理由 两篇arXiv论文详细介绍了改进物理信息神经网络的新颖方法。

在 arXiv cs.AI 阅读 →

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新研究通过学习初始化和LLM指导设计来解决PINN训练失败问题

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报道来源 [3]

  1. arXiv cs.AI TIER_1 English(EN) · Xu Yang, Mingyang Yu, Jing Xu, Keqian Li ·

    进化算法指导的物理信息神经网络设计的LLM

    arXiv:2607.15560v1 Announce Type: cross Abstract: Physics-informed neural networks (PINNs) are unusually sensitive to interacting choices of architecture, activation, loss weighting, collocation, optimization, and constraint enforcement. Large language models (LLMs) can propose t…

  2. arXiv cs.AI TIER_1 English(EN) · Nilay Anurag, Shital Adhikari, Taniya Kapoor, Nikhil Muralidhar ·

    LIGO-PINN：通过门控优化学习初始化以缓解物理信息神经网络中的收敛失败

    arXiv:2607.14233v1 Announce Type: cross Abstract: Physics-informed neural networks (PINNs) have had a broad research impact in modeling domains governed by partial differential equations (PDE). However, PINNs have been shown to perform poorly, sometimes even converging to trivial…

  3. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Keqian Li ·

    进化算法指导的物理信息神经网络设计的LLM

    Physics-informed neural networks (PINNs) are unusually sensitive to interacting choices of architecture, activation, loss weighting, collocation, optimization, and constraint enforcement. Large language models (LLMs) can propose these choices, but independent recommendations do n…

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