Výsledky vyhledávání - "Sarkar, Rajat"

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Hypergraph Learning based Recommender System for Anomaly Detection, Control and Optimization

Autor: Srinivas, Sakhinana Sagar, Sarkar, Rajat Kumar, Runkana, Venkataramana

Publikováno v: IEEE International Conference on Big Data (2022) 1922-1929

Anomaly detection is fundamental yet, challenging problem with practical applications in industry. The current approaches neglect the higher-order dependencies within the networks of interconnected sensors in the high-dimensional time series(multisen

Externí odkaz: http://arxiv.org/abs/2408.11359

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Vision HgNN: An Electron-Micrograph is Worth Hypergraph of Hypernodes

Autor: Srinivas, Sakhinana Sagar, Sarkar, Rajat Kumar, Gangasani, Sreeja, Runkana, Venkataramana

Material characterization using electron micrographs is a crucial but challenging task with applications in various fields, such as semiconductors, quantum materials, batteries, etc. The challenges in categorizing electron micrographs include but are

Externí odkaz: http://arxiv.org/abs/2408.11351

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EMCNet : Graph-Nets for Electron Micrographs Classification

Autor: Srinivas, Sakhinana Sagar, Sarkar, Rajat Kumar, Runkana, Venkataramana

Characterization of materials via electron micrographs is an important and challenging task in several materials processing industries. Classification of electron micrographs is complex due to the high intra-class dissimilarity, high inter-class simi

Externí odkaz: http://arxiv.org/abs/2409.03767

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Battery GraphNets : Relational Learning for Lithium-ion Batteries(LiBs) Life Estimation

Autor: Srinivas, Sakhinana Sagar, Sarkar, Rajat Kumar, Runkana, Venkataramana

Battery life estimation is critical for optimizing battery performance and guaranteeing minimal degradation for better efficiency and reliability of battery-powered systems. The existing methods to predict the Remaining Useful Life(RUL) of Lithium-io

Externí odkaz: http://arxiv.org/abs/2408.07624

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PointSAGE: Mesh-independent superresolution approach to fluid flow predictions

Autor: Sarkar, Rajat, Aripirala, Krishna Sai Sudhir, Jadhav, Vishal Sudam, Sakhinana, Sagar Srinivas, Runkana, Venkataramana

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 behavior and flow patterns, aiding in optimizing design features or e

Externí odkaz: http://arxiv.org/abs/2404.04615

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

Autor: Sarkar, Rajat Kumar, Majumdar, Ritam, Jadhav, Vishal, Sakhinana, Sagar Srinivas, Runkana, Venkataramana

In Computational Fluid Dynamics (CFD), coarse mesh simulations offer computational efficiency but often lack precision. Applying conventional super-resolution to these simulations poses a significant challenge due to the fundamental contrast between

Externí odkaz: http://arxiv.org/abs/2311.09740

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