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08/28/2023
Breaking Boundaries: Distributed Domain Decomposition with Scalable Physics-Informed Neural PDE Solvers
Mosaic Flow is a novel domain decomposition method designed to scale phy...
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07/27/2023
BubbleML: A Multi-Physics Dataset and Benchmarks for Machine Learning
In the field of phase change phenomena, the lack of accessible and diver...
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06/28/2022
Lessons Learned on MPI+Threads Communication
Hybrid MPI+threads programming is gaining prominence, but, in practice, ...
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03/26/2022
NUNet: Deep Learning for Non-Uniform Super-Resolution of Turbulent Flows
Deep Learning (DL) algorithms are becoming increasingly popular for the ...
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08/17/2021
SURFNet: Super-resolution of Turbulent Flows with Transfer Learning using Small Datasets
Deep Learning (DL) algorithms are emerging as a key alternative to compu...
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04/22/2021
Mosaic Flows: A Transferable Deep Learning Framework for Solving PDEs on Unseen Domains
Physics-informed neural networks (PINNs) are increasingly employed to re...
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05/28/2020
Brief Announcement: On the Limits of Parallelizing Convolutional Neural Networks on GPUs
GPUs are currently the platform of choice for training neural networks. ...
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05/09/2020
CFDNet: a deep learning-based accelerator for fluid simulations
CFD is widely used in physical system design and optimization, where it ...
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05/01/2020
How I Learned to Stop Worrying About User-Visible Endpoints and Love MPI
MPI+threads is gaining prominence as an alternative to the traditional M...
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02/07/2020
Breaking Band: A Breakdown of High-performance Communication
The critical path of internode communication on large-scale systems is c...
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02/06/2020
Scalable Communication Endpoints for MPI+Threads Applications
Hybrid MPI+threads programming is gaining prominence as an alternative t...
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02/06/2020