121 citations · 330 across the 20 of their papers we have counts for
20 papers
(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…
EventFlow: Real-Time Neuromorphic Event-Driven Classification of Two-Phase Boiling Flow Regimes
Sanghyeon Chang, Srikar Arani, Nishant Sai Nuthalapati +7
Flow boiling is an efficient heat transfer mechanism capable of dissipating high heat loads with minimal temperature variation, making it an ideal thermal management method. Howeve…
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…
Fused3S: Fast Sparse Attention on Tensor Cores
Zitong Li, Aparna Chandramowlishwaran
Sparse attention is a core building block in many leading neural network models, from graph-structured learning to sparse sequence modeling. It can be decomposed into a sequence of…
Review: Artificial Intelligence for Liquid-Vapor Phase-Change Heat Transfer
Youngjoon Suh, Aparna Chandramowlishwaran, Yoonjin Won
Artificial intelligence (AI) is shifting the paradigm of two-phase heat transfer research. Recent innovations in AI and machine learning uniquely offer the potential for collecting…
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…
Lessons Learned on MPI+Threads Communication
Rohit Zambre, Aparna Chandramowlishwaran
Hybrid MPI+threads programming is gaining prominence, but, in practice, applications perform slower with it compared to the MPI everywhere model. The most critical challenge to the…
NUNet: Deep Learning for Non-Uniform Super-Resolution of Turbulent Flows
Octavi Obiols-Sales, Abhinav Vishnu, Nicholas Malaya +1
Deep Learning (DL) algorithms are becoming increasingly popular for the reconstruction of high-resolution turbulent flows (aka super-resolution). However, current DL approaches per…
SURFNet: Super-resolution of Turbulent Flows with Transfer Learning using Small Datasets
Octavi Obiols-Sales, Abhinav Vishnu, Nicholas Malaya +1
Deep Learning (DL) algorithms are emerging as a key alternative to computationally expensive CFD simulations. However, state-of-the-art DL approaches require large and high-resolut…
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…
Brief Announcement: On the Limits of Parallelizing Convolutional Neural Networks on GPUs
Behnam Pourghassemi, Chenghao Zhang, Joo Hwan Lee +1
GPUs are currently the platform of choice for training neural networks. However, training a deep neural network (DNN) is a time-consuming process even on GPUs because of the massiv…
CFDNet: a deep learning-based accelerator for fluid simulations
Octavi Obiols-Sales, Abhinav Vishnu, Nicholas Malaya +1
CFD is widely used in physical system design and optimization, where it is used to predict engineering quantities of interest, such as the lift on a plane wing or the drag on a mot…
How I Learned to Stop Worrying About User-Visible Endpoints and Love MPI
Rohit Zambre, Aparna Chandramowlishwaran, Pavan Balaji
MPI+threads is gaining prominence as an alternative to the traditional MPI everywhere model in order to better handle the disproportionate increase in the number of cores compared…
Breaking Band: A Breakdown of High-performance Communication
Rohit Zambre, Megan Grodowitz, Aparna Chandramowlishwaran +1
The critical path of internode communication on large-scale systems is composed of multiple components. When a supercomputing application initiates the transfer of a message using…
adPerf: Characterizing the Performance of Third-party Ads
Behnam Pourghassemi, Jordan Bonecutter, Zhou Li +1
Monetizing websites and web apps through online advertising is widespread in the web ecosystem. The online advertising ecosystem nowadays forces publishers to integrate ads from th…
Parallel Performance-Energy Predictive Modeling of Browsers: Case Study of Servo
Rohit Zambre, Lars Bergstrom, Laleh Aghababaie Beni +1
Mozilla Research is developing Servo, a parallel web browser engine, to exploit the benefits of parallelism and concurrency in the web rendering pipeline. Parallelization results i…
Scalable Communication Endpoints for MPI+Threads Applications
Rohit Zambre, Aparna Chandramowlishwaran, Pavan Balaji
Hybrid MPI+threads programming is gaining prominence as an alternative to the traditional "MPI everywhere'" model to better handle the disproportionate increase in the number of co…