5 papers
Cycle-Consistent and Uncertainty-Aware Neural Surrogates for Tokamak Edge Plasmas
Abdourahmane Diaw, Sebastian De Pascuale, Jae-Sun Park +3
The boundary and divertor plasma govern how a tokamak exhausts power and particles, setting heat fluxes, target conditions, and the onset of detachment. Predicting these quantities…
WSINDy for Model Predictive Control with Applications to Fusion, Drones, and Chaos
Cristian López, Mckenna Partridge, Sebastian De Pascuale +4
The control of complex dynamical systems remains a fundamental challenge in science and engineering, where strong nonlinearities, the presence of noise, and computational constrain…
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…
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…
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…