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Found 7 papers, 1 papers with code
Breaking Boundaries: Distributed Domain Decomposition with Scalable Physics-Informed Neural PDE Solvers
Mosaic Flow is a novel domain decomposition method designed to scale physics-informed neural PDE solvers to large domains.
BubbleML: A Multi-Physics Dataset and Benchmarks for Machine Learning
In the field of phase change phenomena, the lack of accessible and diverse datasets suitable for machine learning (ML) training poses a significant challenge.
NUNet: Deep Learning for Non-Uniform Super-Resolution of Turbulent Flows
Due to NUNet's ability to super-resolve only regions of interest, it predicts the same target 1024x1024 spatial resolution 7-28. 5x faster than state-of-the-art DL methods and reduces the memory usage by 4. 4-7. 65x, showcasing improved scalability.
SURFNet: Super-resolution of Turbulent Flows with Transfer Learning using Small Datasets
SURFNet primarily trains the DL model on low-resolution datasets and transfer learns the model on a handful of high-resolution flow problems - accelerating the traditional numerical solver independent of the input size.
Mosaic Flows: A Transferable Deep Learning Framework for Solving PDEs on Unseen Domains
Then, we proposed mosaic flow(MF) predictor, a novel iterative algorithm that assembles the GFNet's inferences for BVPs on large domains with unseen sizes/shapes and BCs while preserving the spatial regularity of the solution.
Brief Announcement: On the Limits of Parallelizing Convolutional Neural Networks on GPUs
However, popular deep learning (DL) frameworks such as TensorFlow and PyTorch launch the majority of neural network operations, especially convolutions, serially on GPUs and do not exploit this inter-op parallelism.
CFDNet: a deep learning-based accelerator for fluid simulations
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 motor vehicle.
