论文标题
使用计算智能来求解Ornstein-zernike方程
Using Computational Intelligence for solving the Ornstein-Zernike equation
论文作者
论文摘要
本论文的主要目的是探索使用计算智能技术来研究简单液体的Ornstein-zernike方程的数值解。特别是，研究了硬球体流体的连续模型。这项贡献有两个主要建议。首先，使用神经网络作为求解Ornstein-zernike方程时参数闭合关系的一种方式。明确显示的是，在硬球体流体的情况下，神经网络方法似乎减少到所谓的超net链链闭合。对于第二个建议，我们探讨了一个事实，即如果将更多的物理信息纳入理论形式主义，则可以通过使用进化优化技术来获得更好的估计。当选择修改的verlet闭合关系并留下一些自由参数要调整时，结果与从分子模拟获得的结果一样好。然后，简要摘要对主要发现和前景进行了简要摘要，以改善此处提出的建议。
The main goal of this thesis is to provide an exploration of the use of computational intelligence techniques to study the numerical solution of the Ornstein-Zernike equation for simple liquids. In particular, a continuous model of the hard sphere fluid is studied. There are two main proposals in this contribution. First, the use of neural networks as a way to parametrize closure relation when solving the Ornstein-Zernike equation. It is explicitly shown that in the case of the hard sphere fluid, the neural network approach seems to reduce to the so-called Hypernetted Chain closure. For the second proposal, we explore the fact that if more physical information is incorporated into the theoretical formalism, a better estimate can be obtained with the use of evolutionary optimization techniques. When choosing the modified Verlet closure relation, and leaving a couple of free parameters to be adjusted, the results are as good as those obtained from molecular simulations. The thesis is then closed with a brief summary of the main findings and outlooks on different ways to improve the proposals presented here.
