基于人工智能的重离子聚变截面数据建模
Modeling Heavy-Ion Fusion Cross Section Data via a Novel Artificial Intelligence Approach
摘要：
我们进行了全面的融合截面数据的分析，目的是推导出，在一个完全的数据驱动的方式，一个模型，适用于预测的集成截面的轻到中等质量的核之间的融合以上的势垒能量。为此，我们采用了一种新的人工智能方法，基于遗传编程和人工神经网络的杂交，能够推导出一个分析模型，用于描述实验数据。该方法使，第一次，执行一个全球性的搜索计算简单的模型在几个变量和大量的核数据。推导出的唯象公式可以用来再现各种各样的轻到中等质量的碰撞系统的融合截面的趋势，在能量域范围约从库仑势垒的多重碎裂现象的发病。
英文摘要：
We perform a comprehensive analysis of complete fusion cross section data with the aim to derive, in a completely data-driven way, a model suitable to predict the integrated cross section of the fusion between light to medium mass nuclei at above barrier energies. To this end, we adopted a novel artificial intelligence approach, based on a hybridization of genetic programming and artificial neural networks, capable to derive an analytical model for the description of experimental data. The approach enables, for the first time, to perform a global search for computationally simple models over several variables and a considerable body of nuclear data. The derived phenomenological formula can serve to reproduce the trend of fusion cross section for a large variety of light to intermediate mass collision systems in an energy domain ranging approximately from the Coulomb barrier to the onset of multi-fragmentation phenomena.
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。
