Search Results for author:
Found 8 papers, 0 papers with code
Can Physics Informed Neural Operators Self Improve?
Physics Informed Neural Operators (PINO) overcome this constraint by utilizing a physics loss for the training, however the accuracy of PINO trained without data does not match the performance obtained by training with data.
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.
How important are specialized transforms in Neural Operators?
Simulating physical systems using Partial Differential Equations (PDEs) has become an indispensible part of modern industrial process optimization.
HyperLoRA for PDEs
Physics-informed neural networks (PINNs) have been widely used to develop neural surrogates for solutions of Partial Differential Equations.
DeepEpiSolver: Unravelling Inverse problems in Covid, HIV, Ebola and Disease Transmission
We then use a neural network to learn the mapping between spread trajectories and coefficients of SIDR in an offline manner.
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.
