English(EN) Bi-HYCO: Bi-Objective Cooperative Learning for PDE Parameter Identification under Fragmented Observations

新的Bi-HYCO框架通过碎片化数据增强偏微分方程参数识别能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 04:00

研究人员推出了一种新颖的协同学习框架Bi-HYCO，用于在观测数据碎片化的情况下识别偏微分方程（PDE）的参数。该方法通过允许互补的物理模型和合成模型在未标记的交互点共享信息来耦合它们，形成一个向量值目标。使用椭圆传输方程和Navier-Stokes方程进行的实验证明了Bi-HYCO在参数和状态重构方面的有效性，即使在噪声条件下也是如此。 AI

影响该框架通过更好地处理不完整的数据，有望提高复杂物理系统仿真的准确性。

排序理由该集群包含一篇详细介绍偏微分方程参数识别新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Umberto Biccari, Jun Chen, Roberto Morales, Enrique Zuazua · 2026-09-10 04:00

Bi-HYCO：碎片化观测下的双目标协同学习用于PDE参数识别

arXiv:2609.06511v1 Announce Type: new Abstract: Physical and synthetic models may describe complementary aspects of the same PDE-governed system while receiving different, possibly fragmented, observations. We propose Bi-Objective HYCO (Bi-HYCO), a cooperative framework that reta…

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报道来源 [1]

Bi-HYCO：碎片化观测下的双目标协同学习用于PDE参数识别

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