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

Semi-Supervised Cascaded Clustering for Classification of Noisy Label Data

no code implementations • 4 May 2022 • Ashit Gupta, Anirudh Deodhar, Tathagata Mukherjee, Venkataramana Runkana

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.

Clustering Missing Labels