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Found 10 papers, 0 papers with code
PointSAGE: Mesh-independent superresolution approach to fluid flow predictions
To address these concerns, we propose a novel framework, PointSAGE a mesh-independent network that leverages the unordered, mesh-less nature of Pointcloud to learn the complex fluid flow and directly predict fine simulations, completely neglecting mesh information.
Redefining Super-Resolution: Fine-mesh PDE predictions without classical simulations
In Computational Fluid Dynamics (CFD), coarse mesh simulations offer computational efficiency but often lack precision.
HyperLoRA for PDEs
Physics-informed neural networks (PINNs) have been widely used to develop neural surrogates for solutions of Partial Differential Equations.
Symbolic Regression for PDEs using Pruned Differentiable Programs
Furthermore, on an average, pruning improves the accuracy of DPA by 7. 81% .
Real-time Health Monitoring of Heat Exchangers using Hypernetworks and PINNs
We demonstrate a Physics-informed Neural Network (PINN) based model for real-time health monitoring of a heat exchanger, that plays a critical role in improving energy efficiency of thermal power plants.
Physics Informed Symbolic Networks
We use this approximation to define multilayer symbolic networks.
Semi-Supervised Cascaded Clustering for Classification of Noisy Label Data
A novel cluster evaluation matrix (CEM) with configurable hyperparameters is introduced to localize and eliminate the noisy labels and invoke a pruning criterion on cascaded clustering.
Multi Scale Graph Wavenet for Wind Speed Forecasting
Geometric deep learning has gained tremendous attention in both academia and industry due to its inherent capability of representing arbitrary structures.
Universal Adversarial Attack on Deep Learning Based Prognostics
To the best of our knowledge, we are the first to study the effect of the universal adversarial perturbation on time series regression models.
Untargeted, Targeted and Universal Adversarial Attacks and Defenses on Time Series
To the best of our knowledge these targeted and universal attacks on time series data have not been studied in any of the previous works.
