English(EN) Physics-informed, boundary-constrained Gaussian process regression for the reconstruction of fluid flow fields

新的高斯过程方法重构流场

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-01 04:00

研究人员开发了一种使用面向物理的高斯过程回归重构流场的新颖方法。该技术将边界约束直接纳入回归过程，从而能够更准确地估计流动动力学。该方法已被证明可以在不需要边界观测的情况下有效模拟空气动力学剖面周围的流体行为。 AI

排序理由该集群包含一篇详细介绍新方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

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

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arXiv stat.ML TIER_1 English(EN) · Adrian Padilla-Segarra, Pascal Noble, Olivier Roustant, \'Eric Savin · 2026-06-01 04:00

Physics-informed, boundary-constrained Gaussian process regression for the reconstruction of fluid flow fields

arXiv:2507.17582v4 Announce Type: replace-cross Abstract: Gaussian process regression techniques have been used in fluid mechanics for the reconstruction of flow fields from a reduction-of-dimension perspective. A main ingredient in this setting is the construction of adapted cov…