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English(EN) Adaptive surrogate modeling for high-dimensional spatio-temporal output

新的自适应代理建模方法处理高维时空数据

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新的自适应代理建模方法，旨在处理具有极高维时空输出的问题。该方法首先将输出数据的维度降低到低维潜在空间，然后再构建代理模型。该方法随后自适应地识别新的训练点，以最少的昂贵物理模型调用来提高模型的准确性，并通过燃气轮机叶片的 termo-mechanical 分析证明了其有效性。 AI

影响 该方法通过降低计算成本，有望提高工程和科学领域复杂模拟的效率。

排序理由 该集群包含一篇在 arXiv 上发表的关于新方法的论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv stat.ML 阅读 →

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

新的自适应代理建模方法处理高维时空数据

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该集群包含一篇在 arXiv 上发表的关于新方法的论文。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Berkcan Kapusuzoglu, Shunsaku Matsumoto, Yoshitomo Miyagi, Daigo Watanabe, Sankaran Mahadevan ·

    高维时空输出的自适应代理建模

    arXiv:2608.17250v1 Announce Type: cross Abstract: This paper develops an adaptive surrogate modeling method for problems with very high-dimensional spatio-temporal outputs. The analysis of spatio-temporal multi-physics systems is computationally expensive and consists of a large …

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