Diagram of beam distribution of the magnetic trap at 50 ns with different beam incidence velocities. Green points represent protons, red points represent electrons and blue lines represent magnetic induction lines. Part of Fig. 6 of the paper by Fang-Ping Wang et al. https://doi.org/10.1002/ctpp.202400040
Normalized vector potential (ay), the produced wakefield (Ex), the momentum (px) and the electron density (nx) for hydrogen atoms density/pre-ionized plasma density n0 = 0.01ncr at time 810 fs for the field-ionized plasma (panel a) and the pre-ionized plasma (panel b) and normalized vector potential variation a0 = 1 (in panels a1 and b1), a0 = 2 (in panels a2 and b2), and a0 = 3 (in panels a3 and b3). Fig. 4 of the paper by Elnaz Khalilzadeh et al. https://doi.org/10.1002/ctpp.202400022
Plot of temperature inside the rock as a function of time at 70 kV. Fig. 8 of the paper by X. Zhu et al. https://onlinelibrary.wiley.com/doi/10.1002/ctpp.202400058
In this study, we consider three different machine-learning methods—a three-hidden-layer neural network, support vector regression, and Gaussian process regression—and compare how well they can learn from a synthetic data set for proton acceleration in the Target Normal Sheath Acceleration regime. The synthetic data set was generated from a previously published theoretical model by Fuchs et al. 2005 that we modified. Once trained, these machine-learning methods can assist with efforts to maximize the peak proton energy, or with the more general problem of configuring the laser system to produce a proton energy spectrum with desired characteristics. In our study, we focus on both the accuracy of the machine-learning methods and the performance on one GPU including memory consumption. Although it is arguably the least sophisticated machine-learning model we considered, support vector regression performed very well in our tests.
Breakdown field distortion of three liquid media: (a) Electric field under deionized water; (b) electric field underwater-based drilling fluid action; (c) electric field under oil-based drilling fluid action. Fig. 7 of the paper by X. Zhu et al. https://onlinelibrary.wiley.com/doi/10.1002/ctpp.202400035
F. X. Bronold and F. Willert. About the quantum-kinetic derivation of boundary conditions for quasiparticle Boltzmann equations at interfaces. Contributions to Plasma Physics. 2024; 64:e202300168. https://doi.org/10.1002/ctpp.202300168.
We sincerely regret the lapses.
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：