New Mesh-Native Convolution Adapts Foundation Models for Unstructured Data

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-12 04:00

研究人员推出了一种新颖的 tokenization 层 Mesh-Native Convolution (MNC)，旨在使基础模型适应非结构化网格数据。该方法允许在网格数据上训练的模型通过最少的重新训练，高效地改造以适应不规则的非结构化网格。MNC 旨在弥合卷积神经网络 (CNN) 和图神经网络 (GNN) 之间的差距，从而提高物理基础模型在不同数据表示之间的可移植性。 AI

影响使基础模型能够更轻松地应用于具有非结构化数据的更广泛的科学计算问题。

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New Mesh-Native Convolution Adapts Foundation Models for Unstructured Data

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arXiv cs.AI TIER_1 English(EN) · Georg Maierhofer, Miles Cranmer, Payel Mukhopadhyay · 2026-10-12 04:00

Mesh-Native Tokenization: Retrofitting Foundation Models to Unstructured Data

arXiv:2610.12618v1 Announce Type: cross Abstract: Physics foundation models are an increasingly popular approach for building versatile emulators for spatiotemporal systems. They require tokenisation layers that lift physical field data into a latent representation suitable for d…

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Mesh-Native Tokenization: Retrofitting Foundation Models to Unstructured Data

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