Scientists Look to Exascale and Deep Learning for Developing Sustainable Fusion Energy
Scientists Look to Exascale and Deep Learning for Developing Sustainable Fusion Energy
Published
Publication InsideHPC
Award Aurora ESP
Systems Aurora | Sunspot
Princeton’s Fusion Recurrent Neural Network (FRNN) code uses convolutional and recurrent neural network components to integrate both spatial and temporal information for predicting disruptions in tokamak plasmas with unprecedented accuracy and speed on top supercomputers. (Image: Eliot Feibush, Princeton Plasma Physics Laboratory)