New framework tracks topological features directly in continuous implicit models

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-14 04:00

研究人员开发了一个新框架，用于在连续隐式模型（如隐式神经网络表示（INRs）和多元函数逼近（MFAs））中直接跟踪拓扑特征。该方法避免了将数据重采样到离散网格上的需要，从而防止了离散化伪影，并实现了更平滑、更连贯的临界点轨迹。该框架被证明是通用的，可用于各种隐式表示，并支持围绕这些模型的新功能驱动的可视化工作流程。 AI

影响能够更准确地跟踪和可视化由隐式模型表示的科学数据。

排序理由这是一篇描述新计算框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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New framework tracks topological features directly in continuous implicit models

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Guanqun Ma, David Lenz, Kaiyuan Tang, Hanqi Guo, Chaoli Wang, Tom Peterka, Bei Wang · 2026-09-14 04:00

连续隐式模型中的直接拓扑跟踪

arXiv:2609.12157v1 Announce Type: cross Abstract: We present a framework for tracking topological features directly within continuous implicit models. Such models, including implicit neural representations (INRs) and multivariate functional approximations (MFAs), are increasingly…

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报道来源 [1]

连续隐式模型中的直接拓扑跟踪

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