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English(EN) Spectral Embedding via Chebyshev Bases for Robust DeepONet Approximation

新的SEDONet架构增强了科学计算中AI的逼近能力

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新颖的谱嵌入深度算子网络（SEDONet）架构，以提高DeepONets在科学计算复杂问题上的逼近能力。与使用原始坐标上的全连接层的标准DeepONets不同，SEDONet采用了切比雪夫谱字典，为有界域提供了合适的归纳偏置。这种方法使SEDONet能够更好地捕捉精细尺度特征、边界层和非周期结构，在包括二维泊松方程和Lorenz-96混沌系统在内的各种基准测试中，其相对L2误差优于基线DeepONets和傅里叶嵌入变体。 AI

影响 这种新架构可能带来更准确、更高效的AI驱动的复杂科学模拟代理模型。

排序理由 详细介绍一种用于科学计算的新型AI模型架构的学术论文。[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SEDONet架构增强了科学计算中AI的逼近能力

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详细介绍一种用于科学计算的新型AI模型架构的学术论文。[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) · Muhammad Abid, Omer San ·

    基于切比雪夫基的谱嵌入用于鲁棒DeepONet近似

    arXiv:2512.09165v2 Announce Type: replace Abstract: Deep Operator Networks (DeepONets) have emerged as a powerful framework for data-driven operator learning, providing flexible surrogates for nonlinear mappings arising in partial differential equations (PDEs). However, the stand…

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