English(EN) Can Deep Learning Achieve Cross-Physics Mapping?

深度学习框架实现了不同物理域之间的跨物理映射

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-16 04:00

研究人员推出了一种新颖的算子学习框架——跨物理映射（CPM），该框架使深度学习模型能够在由不同方程控制的物理域之间进行转换。该研究通过兼容的潜在表示和无量纲缩放原理提出了此类映射的条件。扩散场和波场的实验表明，虽然波场到扩散场的映射更稳定，但扩散场到波场的映射更具挑战性，其中U-NO架构在后一种情况下表现最佳。 AI

影响这项研究通过使人工智能能够连接不同的物理现象，有可能在科学建模和模拟中实现新的应用。

排序理由该集群包含一篇详细介绍算子学习新框架和实验结果的研究论文。[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) · Pengfei Zhu, Julien Lecompagnon, Mathias Ziegler · 2026-09-16 04:00

深度学习能否实现跨物理映射？

arXiv:2609.16853v1 Announce Type: new Abstract: Can deep learning translate physical fields governed by fundamentally different equations? We address this question by introducing Cross-Physics Mapping (CPM), an operator-learning framework for mappings between heterogeneous physic…

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深度学习能否实现跨物理映射？

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