A Physics-Informed Deep Learning Description of Knudsen Layer Reactivity Reduction
arxiv(2024)
摘要
A physics-informed neural network (PINN) is used to evaluate the fast ion
distribution in the hot spot of an inertial confinement fusion target. The
employment of customized input and output layers to the neural network are
shown to enable a PINN to learn the parametric solution to the
Vlasov-Fokker-Planck equation in the absence of any synthetic or experimental
data. While the offline training of the PINN is computationally intensive,
online deployment is rapid, thus enabling an efficient surrogate of the fast
ion tail across a broad range of hot spot conditions. As an explicit
demonstration of the approach, the specific problem of Knudsen layer fusion
yield reduction is treated. Here, predictions from the Vlasov-Fokker-Planck
PINN are used to provide a non-perturbative solution of the fast ion tail in
the vicinity of the hot spot thus allowing the spatial profile of the fusion
reactivity to be evaluated for a range of collisionalities and hot spot
conditions. Excellent agreement is found between the predictions of the
Vlasov-Fokker-Planck PINN and results from traditional numerical solvers with
respect to both the energy and spatial distribution of fast ions and the fusion
reactivity profile demonstrating that the Vlasov-Fokker-Planck PINN provides an
accurate and efficient means of determining the impact of Knudsen layer yield
reduction across a broad range of plasma conditions.
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