4 papers · 1 filter
(HB-ARFM) History-Bootstrapped Flow Matching for Inverse Boiling Reconstruction
Xianwei Zou, Sheikh Md Shakeel Hassan, Arthur Feeney +1
Reconstructing spatiotemporal fields from partial observations is fundamental to scientific inference, from inferring atmospheric states from satellite data to recovering fluid sta…
NUCLEUS-MoE: Unified Model of Pool Boiling for Liquid Cooling
Arthur Feeney, Xianwei Zou, Sheikh Md Shakeel Hassan +2
Two-phase boiling enables heat transfer rates an order of magnitude higher than single-phase cooling, but it remains difficult to model due to the strong coupling between phase cha…
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…