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physics.plasm-ph2026
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…
physics.plasm-ph2026
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,…
physics.plasm-ph2025
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…