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Found 5 papers, 1 papers with code
An Estimator for the Sensitivity to Perturbations of Deep Neural Networks
For Deep Neural Networks (DNNs) to become useful in safety-critical applications, such as self-driving cars and disease diagnosis, they must be stable to perturbations in input and model parameters.
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.
Automating Artifact Detection in Video Games
Based on a sample of representative screen corruption examples, the model was able to identify 10 of the most commonly occurring screen artifacts with reasonable accuracy.
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.
