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.
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% .
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.
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.
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.