121 citations · 330 across the 20 of their papers we have counts for
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(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…
Bubbleformer: Forecasting Boiling with Transformers
Sheikh Md Shakeel Hassan, Xianwei Zou, Akash Dhruv +2
Modeling boiling (an inherently chaotic, multiphase process central to energy and thermal systems) remains a significant challenge for neural PDE surrogates. Existing models requir…
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…
Mosaic Flows: A Transferable Deep Learning Framework for Solving PDEs on Unseen Domains
Hengjie Wang, Robert Planas, Aparna Chandramowlishwaran +1
Physics-informed neural networks (PINNs) are increasingly employed to replace/augment traditional numerical methods in solving partial differential equations (PDEs). While state-of…