<ul><li>Operator learning enables data-driven modeling of partial differential equations by learning mappings between function spaces.</li><li>Mondrian introduces transformer operators that decompose a domain into non-overlapping subdomains and apply attention over sequences of subdomain-restricted functions.</li><li>This approach decouples attention from discretization and supports local and global interactions through hierarchical windowed and neighborhood attention.</li><li>Mondrian achieves strong performance on Allen-Cahn and Navier-Stokes PDEs, showcasing resolution scaling without retraining.</li></ul>

Mondrian: Transformer Operators via Domain Decomposition

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