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English(EN) Rationally Enriched Chebyshev Trunk Bases for DeepONet Surrogates of High Péclet Entrance Transport

新的DeepONet代理模型提升输运问题预测能力

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种名为理性增强切比雪夫（REC）树的新型代理模型，用于DeepONet，旨在处理具有薄边界层的高Péclet输运问题。该REC树将切比雪夫多项式与源自自适应Antoulas-Anderson（AAA）算法的理性元素相结合。评估显示，与标准的切比雪夫树DeepONet相比，REC树DeepONet在标量剖面预测精度方面提高了高达19.5%，在温度和浓度剖面预测精度方面分别提高了高达60.2%和32.2%，同时还减少了振荡。 AI

影响 代理建模的这项进展可能导致在涉及复杂输运现象的领域中实现更准确、更高效的模拟。

排序理由 该集群描述了一篇学术论文中提出的新颖方法，用于改进特定类型的机器学习模型（DeepONet）在科学模拟任务中的应用。

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新的DeepONet代理模型提升输运问题预测能力

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该集群描述了一篇学术论文中提出的新颖方法，用于改进特定类型的机器学习模型（DeepONet）在科学模拟任务中的应用。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Mingeun Choi, Satish Kumar ·

    深度算子网络代理高Péclet入口输运的理性富集切比雪夫树基

    arXiv:2608.19658v1 Announce Type: new Abstract: This study demonstrates a rationally enriched Chebyshev (REC) trunk for deep operator network (DeepONet) surrogate models of singularly perturbed and high-P\'eclet transport problems whose solution profiles are characterized by thin…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    深度算子网络代理高Péclet入口输运的理性富集切比雪夫树基

    This study demonstrates a rationally enriched Chebyshev (REC) trunk for deep operator network (DeepONet) surrogate models of singularly perturbed and high-Péclet transport problems whose solution profiles are characterized by thin localized boundary or wall layers. The REC trunk …

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