Training an artificial neural network for recognizing electron collision patterns

DOI10.1016/J.PHYSLETA.2020.127005zbMath1482.82049OpenAlexW3098466709MaRDI QIDQ822558

Jongeun Choi, Jaehyun Nam, Hwanmoo Yong, Jungho Hwang

Publication date: 22 September 2021

Published in: Physics Letters. A (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.physleta.2020.127005

zbMATH Keywords

neural networksmachine learningBayesian optimizationinverse designelectron scattering cross sectionplasma modelling

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Bayesian inference (62F15) Neural nets applied to problems in time-dependent statistical mechanics (82C32) (2)-body potential quantum scattering theory (81U05) Statistical mechanics of plasmas (82D10) Monte Carlo methods applied to problems in statistical mechanics (82M31)

Uses Software

• ElemStatLearn
• Bolsig
• Spearmint
• ImageNet
• AlexNet

Cites Work

• Unnamed Item
• A comparative study of ordinary cross-validation, v-fold cross-validation and the repeated learning-testing methods
• Convergence rates of efficient global optimization algorithms
• Random forests

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