5 papers
Interpretable statistical feature engineering for early disruption prediction in the short pulse ADITYA tokamak
Jyoti Agarwal, Kavit Patel, Bhaskar Chaudhury +3
Reliable early disruption prediction is critical for the safe operation and real-time control of tokamaks. However, machine learning based prediction frameworks have predominantly…
Data-Driven Reconstruction of Spatially Resolved Electron and Ion Energy Distributions from Macroscopic Plasma Quantities with Deep Neural Networks
Libin Varghese, Kaushik Prajapati, Bhaskar Chaudhury
Spatially resolved EEDFs/IEDFs provide essential kinetic information about low-temperature plasmas (LTPs) and play a central role in determining transport, chemical reaction rates,…
Early Prediction of Current Quench Events in the ADITYA Tokamak using Transformer based Data Driven Models
Jyoti Agarwal, Bhaskar Chaudhury, Jaykumar Navadiya +2
Disruptions in tokamak plasmas, marked by sudden thermal and current quenches, pose serious threats to plasma-facing components and system integrity. Accurate early prediction, wit…
Benchmarking and Parallelization of Electrostatic Particle-In-Cell for low-temperature Plasma Simulation by particle-thread Binding
Libn Varghese, Bhaskar Chaudhury, Miral Shah +1
The Particle-In-Cell (PIC) method for plasma simulation tracks particle phase space information using particle and grid data structures. High computational costs in 2D and 3D devic…
Capability enhancement of the X-ray micro-tomography system via ML-assisted approaches
Dhruvi Shah, Shruti Mehta, Ashish Agrawal +2
Ring artifacts in X-ray micro-CT images are one of the primary causes of concern in their accurate visual interpretation and quantitative analysis. The geometry of X-ray micro-CT s…