Redefining Super-Resolution: Fine-mesh PDE predictions without classical simulations

no code implementations • 16 Nov 2023 • Rajat Kumar Sarkar, Ritam Majumdar, Vishal Jadhav, Sagar Srinivas Sakhinana, Venkataramana Runkana

In Computational Fluid Dynamics (CFD), coarse mesh simulations offer computational efficiency but often lack precision.

Computational Efficiency Super-Resolution

HyperLoRA for PDEs

no code implementations • 18 Aug 2023 • Ritam Majumdar, Vishal Jadhav, Anirudh Deodhar, Shirish Karande, Lovekesh Vig, Venkataramana Runkana

Physics-informed neural networks (PINNs) have been widely used to develop neural surrogates for solutions of Partial Differential Equations.

Meta-Learning regression

Symbolic Regression for PDEs using Pruned Differentiable Programs

no code implementations • 13 Mar 2023 • Ritam Majumdar, Vishal Jadhav, Anirudh Deodhar, Shirish Karande, Lovekesh Vig, Venkataramana Runkana

Furthermore, on an average, pruning improves the accuracy of DPA by 7. 81% .

regression Symbolic Regression

Real-time Health Monitoring of Heat Exchangers using Hypernetworks and PINNs

no code implementations • 20 Dec 2022 • Ritam Majumdar, Vishal Jadhav, Anirudh Deodhar, Shirish Karande, Lovekesh Vig, Venkataramana Runkana

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

no code implementations • 11 Jul 2022 • Ritam Majumdar, Vishal Jadhav, Anirudh Deodhar, Shirish Karande, Lovekesh Vig, Venkataramana Runkana

We use this approximation to define multilayer symbolic networks.

Inductive Bias