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English(EN) Multi-Head Residual-Gated DeepONet for Coherent Nonlinear Wave Dynamics

新的MH-RG DeepONet架构改进了非线性波动力学建模

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新颖的多头残差门控DeepONet (MH-RG)架构，旨在更好地模拟相干非线性波动力学。该新框架将初始状态的紧凑物理描述符整合为残差调制因子，以补充标准的DeepONet状态路径。MH-RG架构包含一个预分支残差调制器、一个分支残差门和一个带有低秩多头机制的干路残差门，以有效地捕捉多种条件响应模式。在涉及非线性保守和耗散动力学的基准测试中进行的评估表明，与现有的特征增强基线相比，MH-RG DeepONet实现了更低的误差率，同时更好地保留了相位相干性和关键动力学量。 AI

影响 引入了一种新颖的神经网络架构，用于改进复杂物理动力学的建模，可能推动科学模拟和预测的发展。

排序理由 详细介绍新模型架构的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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

新的MH-RG DeepONet架构改进了非线性波动力学建模

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详细介绍新模型架构的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Zhiwei Fan, Yiming Pan, Daniel Coca ·

    用于相干非线性波动力学的多头残差门控深度ONet

    arXiv:2604.11972v2 Announce Type: replace Abstract: Coherent nonlinear wave dynamics are often strongly shaped by a compact set of physically meaningful descriptors of the initial state. Traditional neural operators typically treat the input-output mapping as a largely black-box …

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