3 citations · 7 across the 5 of their papers we have counts for
5 papers
Redefining Super-Resolution: Fine-mesh PDE predictions without classical simulations
Rajat Kumar Sarkar, Ritam Majumdar, Vishal Jadhav +2
In Computational Fluid Dynamics (CFD), coarse mesh simulations offer computational efficiency but often lack precision. Applying conventional super-resolution to these simulations…
HyperLoRA for PDEs
Ritam Majumdar, Vishal Jadhav, Anirudh Deodhar +3
Physics-informed neural networks (PINNs) have been widely used to develop neural surrogates for solutions of Partial Differential Equations. A drawback of PINNs is that they have t…
Symbolic Regression for PDEs using Pruned Differentiable Programs
Ritam Majumdar, Vishal Jadhav, Anirudh Deodhar +3
Physics-informed Neural Networks (PINNs) have been widely used to obtain accurate neural surrogates for a system of Partial Differential Equations (PDE). One of the major limitatio…
Real-time Health Monitoring of Heat Exchangers using Hypernetworks and PINNs
Ritam Majumdar, Vishal Jadhav, Anirudh Deodhar +3
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 o…
Physics Informed Symbolic Networks
Ritam Majumdar, Vishal Jadhav, Anirudh Deodhar +3
We introduce Physics Informed Symbolic Networks (PISN) which utilize physics-informed loss to obtain a symbolic solution for a system of Partial Differential Equations (PDE). Given…