Knudsen层反应性降低的物理信息深度学习描述
A Physics-Informed Deep Learning Description of Knudsen Layer Reactivity Reduction
AI总结：
本文提出利用定制的物理信息神经网络（PINN）求解Vlasov-Fokker-Planck方程，无需实验数据即可准确高效预测惯性约束聚变热点中Knudsen层导致的聚变产额降低现象。
摘要：
使用物理信息神经网络（PINN）来评估惯性约束聚变靶点热点中的快离子分布。研究表明，为神经网络定制输入和输出层，使得PINN能够在没有任何合成或实验数据的情况下学习Vlasov-Fokker-Planck方程的参数化解。作为该方法的显式演示，本文处理了Knudsen层聚变产额降低的具体问题。在此，Vlasov-Fokker-Planck PINN的预测被用于提供热点附近快离子尾的非微扰解，从而允许在一系列碰撞率和热点条件下评估聚变反应性的空间分布。Vlasov-Fokker-Planck PINN的预测与传统数值求解器的结果在快离子的能量和空间分布以及聚变反应性分布方面均表现出极好的一致性，这表明Vlasov-Fokker-Planck PINN提供了一种准确且高效的方法来确定Knudsen层产额降低在广泛等离子体条件下的影响。
英文摘要：
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 use of tailored input and output layers to the neural network is 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. 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
