English(EN) Spectral-Embedded Operator Learning for Three-Phase Interfacial Flow: A Ternary Cahn-Hilliard-Navier-Stokes Benchmark

新的基准测试用于检验算子学习在复杂流体动力学中的应用

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

研究人员开发了一个用于流体动力学算子学习模型的新基准测试，特别关注三相界面流动。该基准测试利用一个三元 Cahn-Hilliard-Navier-Stokes 求解器，为诸如气泡刺穿水油界面等复杂场景生成参考数据。研究比较了三种 DeepONet 变体，发现使用切比雪夫表示的 SEDONet 在准确性和误差减少方面显著优于其他模型，尤其是在界面附近和气泡突破后。 AI

影响这项研究推进了复杂物理模拟的算子学习技术，有望提高流体动力学建模的准确性。

排序理由该集群包含一篇学术论文，详细介绍了一个针对特定科学问题的新的基准测试和模型比较。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Muhammad Abid, Arth Sojitra, Omer San · 2026-09-01 04:00

三相界面流动的谱嵌入算子学习：一个三元 Cahn-Hilliard-Navier-Stokes 基准

arXiv:2608.29069v1 Announce Type: cross Abstract: Operator-learning surrogates have been benchmarked largely on single-field, single-interface problems, leaving unclear whether architectural choices validated in those settings transfer to constrained, multiphase flows. We introdu…

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三相界面流动的谱嵌入算子学习：一个三元 Cahn-Hilliard-Navier-Stokes 基准

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