plasma physics
Projects with this topic

CURRENTLY UNDER CONSTRUCTION! This paper focuses on the construction of nonintrusive Scientific Machine Learning (SciML) ReducedOrder Models (ROMs) for nonlinear, chaotic plasma turbulence simulations. In particular, we propose using Operator Inference (OpInf) to build lowcost physicsbased ROMs from data for such simulations. As a representative example, we focus on the HasegawaWakatani (HW) equations used for modeling twodimensional electrostatic driftwave plasma turbulence. For a comprehensive perspective of the potential of OpInf to construct accurate ROMs for this model, we consider a setup for the HW equations that leads to the formation of complex, nonlinear and selfdriven dynamics, and perform two sets of experiments. We first use the data obtained via a direct numerical simulation of the HW equations starting from a specific initial condition and train OpInf ROMs for predictions beyond the training time horizon. In the second, more challenging set of experiments, we train ROMs using the same data set as before but this time perform predictions for six other initial conditions. Our results show that the OpInf ROMs capture the important features of the turbulent dynamics and generalize to new and unseen initial conditions while reducing the evaluation time of the highfidelity model by up to six orders of magnitude in singlecore performance. In the broader context of fusion research, this shows that nonintrusive SciML ROMs have the potential to drastically accelerate numerical studies, which can ultimately enable tasks such as the design and realtime control of optimized fusion devices.
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Opensource Python package for geometric plasma simulations.
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NN surrogate model of the idealMHD code VMEC.
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The w7x package is a simulation framework that defines interfaces for simulation codes with interchangeable backends, computation pipelines in a directed acyclic graph that communicate via a central state.
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