AI RESEARCH
MeshTok: Efficient Multi-Scale Tokenization for Scalable PDE Transformers
arXiv CS.LG
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ArXi:2606.04366v1 Announce Type: new Conventional patchified Transformers operate on uniform spatial partitions, distributing computational effort evenly across the domain irrespective of local features. This inflexible tokenization scheme is inherently limited in its ability to efficiently represent and process solutions to complex PDEs. To address this, we propose MeshTok, an adaptive mesh refinement (AMR)-inspired tokenization and sequence modeling framework.