6 citations · 10 across the 8 of their papers we have counts for
5 papers · 1 filter
EMCNet : Graph-Nets for Electron Micrographs Classification
Sakhinana Sagar Srinivas, Rajat Kumar Sarkar, Venkataramana Runkana
Characterization of materials via electron micrographs is an important and challenging task in several materials processing industries. Classification of electron micrographs is co…
Hypergraph Learning based Recommender System for Anomaly Detection, Control and Optimization
Sakhinana Sagar Srinivas, Rajat Kumar Sarkar, Venkataramana Runkana
Anomaly detection is fundamental yet, challenging problem with practical applications in industry. The current approaches neglect the higher-order dependencies within the networks…
Vision HgNN: An Electron-Micrograph is Worth Hypergraph of Hypernodes
Sakhinana Sagar Srinivas, Rajat Kumar Sarkar, Sreeja Gangasani +1
Material characterization using electron micrographs is a crucial but challenging task with applications in various fields, such as semiconductors, quantum materials, batteries, et…
Battery GraphNets : Relational Learning for Lithium-ion Batteries(LiBs) Life Estimation
Sakhinana Sagar Srinivas, Rajat Kumar Sarkar, Venkataramana Runkana
Battery life estimation is critical for optimizing battery performance and guaranteeing minimal degradation for better efficiency and reliability of battery-powered systems. The ex…
PointSAGE: Mesh-independent superresolution approach to fluid flow predictions
Rajat Sarkar, Krishna Sai Sudhir Aripirala, Vishal Sudam Jadhav +2
Computational Fluid Dynamics (CFD) serves as a powerful tool for simulating fluid flow across diverse industries. High-resolution CFD simulations offer valuable insights into fluid…