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English(EN) Wave Physics as Representation: Tensor-Structured Neural Reconstruction for Ocean Acoustic Fields

TIDAL神经网络利用物理导出的表示来重建海洋声场

研究人员开发了TIDAL,这是一种新颖的神经网络,旨在从稀疏测量中重建复杂的3D海洋声场。与仅依赖数据或强制执行完整物理方程的传统方法不同,TIDAL利用一种物理导出的张量表示(称为S/c表示)作为归纳偏置。这种方法整合了波物理原理,而无需特定的环境参数,从而在数据有限的条件下提高了重建性能和推理速度。 AI

影响 这项研究可能带来更有效、更准确的分析水下环境的方法,在声纳、海洋生物学和水下通信方面具有潜在应用。

排序理由 该集群包含一篇学术论文,详细介绍了一种使用新颖神经网络架构重建海洋声场的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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

TIDAL神经网络利用物理导出的表示来重建海洋声场

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该集群包含一篇学术论文,详细介绍了一种使用新颖神经网络架构重建海洋声场的新方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yifan Sun, Panqi Chen, Weifeng Shi, Xiao Fu, Lei Cheng ·

    波物理作为表征:用于海洋声场的张量结构神经重建

    arXiv:2611.01680v2 Announce Type: replace-cross Abstract: Reconstructing three-dimensional (3D) complex ocean acoustic fields from sparse measurements is challenging due to highly oscillatory wave structures and restricted sensing patterns/routes. Learning-based approaches fall i…