3 papers
physics.plasm-ph2026
A machine learning framework for developing quasilinear saturation rules of turbulent transport from linear gyrokinetic data
Preeti Sar, Sebastian De Pascuale, Harry Dudding +1
A new neural network model for a quasilinear saturation rule has been developed to map linear gyrokinetic data to nonlinear saturated potential magnitudes to predict the total ener…
physics.plasm-ph2025
Autoregressive long-horizon prediction of plasma edge dynamics
Hunor Csala, Sebastian De Pascuale, Paul Laiu +3
Accurate modeling of scrape-off layer (SOL) and divertor-edge dynamics is vital for designing plasma-facing components in fusion devices. High-fidelity edge fluid/neutral codes suc…
physics.comp-ph2025
Exploring the Capabilities of the Frontier Large Language Models for Nuclear Energy Research
Ahmed Almeldein, Mohammed Alnaggar, Rick Archibald +47
The AI for Nuclear Energy workshop at Oak Ridge National Laboratory evaluated the potential of Large Language Models (LLMs) to accelerate fusion and fission research. Fourteen inte…