Particle deposition on human air pathway using Turbulence Modelling
Authors: Thivin Anandh, Lokesh M, Prof. Sashikumaar Ganesan
This project involves modelling of the turbulent fluid flow in the human air pathway geometry using Turbulence modelling methods and then coupling them with the Lagrangian approach of tracking particles in that fluid flow to identify the deposition patterns (fractions) in within the geometry.
- Arbitrary Lagrangian Eulerian (ALE) framework using In compressible Navier Stokes Equations for Moving Domain Problems
Authors: Thivin Anandh, Prof. Sashikumaar Ganesan
AI Augmented large scale simulation of wind farms using PINNS & ROM
Authors: Thivin Anandh, Divij Ghose, Protyush, Rajarshi
Machine Learning System For Combat Aircraft Applications
Authors: Lokesh Mohanty, Protyush Pradyut Chowdhury, Prof. Sashikumaar Ganesan
The project entails designing and implementing ML models for diverse tasks including signal clustering, emitter classification, multi-sensor data fusion, voice command analysis, enemy radio intercept analysis, target detection in video/images, and a pilot associate application. These models aim to enhance detection, classification, identification, localization, direction of arrival estimation, and tracking using outputs from sensors like EO-IR, RADAR, RWR, and MAWS. Additionally, the project emphasizes optimizing ML models for high accuracy, adaptability, and real-time performance. Implementation of MLOps principles ensures efficient development, deployment, and monitoring of the ML system.