Neural network accelerates plasma simulations

By combining a deep understanding of plasma physics with machine learning techniques, DIFFER researchers developed a new ultrafast neural network model of the turbulent plasma in a fusion reactor. The neural network can accurately predict heat and particle transport in the fusion reactor up to 100.000 times faster than before: a vital tool to optimize the performance of future fusion power plants. Fusion reactors are fuelled by a plasma: a hot, ionized gas of hydrogen isotopes that fuse together at extreme temperatures to form helium and release clean energy. The behavior of the plasma is not easy to predict: the charged plasma particles respond not only to the magnetic field that keeps them trapped inside the reactor, but also to the electromagnetic fields they create themselves through their own motion. That makes predicting a fusion plasma in order to optimize its state a difficult but rewarding problem to tackle.