2 citations · 2 across the 3 of their papers we have counts for
3 papers
Mondrian: Transformer Operators via Domain Decomposition
Arthur Feeney, Kuei-Hsiang Huang, Aparna Chandramowlishwaran
Operator learning enables data-driven modeling of partial differential equations (PDEs) by learning mappings between function spaces. However, scaling transformer-based operator mo…
Breaking Boundaries: Distributed Domain Decomposition with Scalable Physics-Informed Neural PDE Solvers
Arthur Feeney, Zitong Li, Ramin Bostanabad +1
Mosaic Flow is a novel domain decomposition method designed to scale physics-informed neural PDE solvers to large domains. Its unique approach leverages pre-trained networks on sma…
BubbleML: A Multi-Physics Dataset and Benchmarks for Machine Learning
Sheikh Md Shakeel Hassan, Arthur Feeney, Akash Dhruv +5
In the field of phase change phenomena, the lack of accessible and diverse datasets suitable for machine learning (ML) training poses a significant challenge. Existing experimental…