• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文学位 >Towards more reliable extrapolation algorithms with applications to organic chemistry.
【24h】

Towards more reliable extrapolation algorithms with applications to organic chemistry.

机译：寻求更可靠的外推算法，并将其应用于有机化学。

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

One of the main objectives of science and engineering is to predict the results of different situations. For example, in Newton's mechanics, we want to predict the positions and velocities of different objects (e.g., planets) at future moments of time.;In this thesis, as a case study, we take the problem of predicting the properties of new chemical substances. One of the main objectives of chemistry is to design new molecules (and, more generally, new chemical compounds) which are useful for various practical tasks. New substances have already resulted in new materials for buildings and for spaceships, new explosives and new fuels, new medicines, etc. New compounds are being designed and tested all the time.;For example, this is how new medicines are designed: a large number of different promising substances are synthesized and tested, but only a few turn out to be practically useful. Synthesizing a new compound is often difficult and time-consuming. It is therefore desirable to predict the properties of new compounds, so as to filter out the ones which do not have the desired properties.;In physics, usually, we know the exact equations that describe the objects of interest, and we know how to solve these equations. This is the case for Newton's mechanics. In such situations, we face a purely mathematical problem: to solve these equations and thus compute the value y of the desired characteristic based on the known values of the parameters x1,...,xn that describe the given objects.;In many other application areas, we either do not know the equations, or the equations are so complex that we do not know how to solve them. For example, in chemistry, in principle, we can use the equations of quantum mechanics to describe an arbitrary chemical substance, but in practice, especially for organic substances, these equations are too complex to solve.;In the situations in which we do not know the equations -- or we do not know how to solve the equations---the prediction problem takes the following form: (1) we know the values of a quantity v(a) for some objects a, and (2) we want to predict the values of this quantity for some other objects a'.;There are many examples of successful predictions in science. In many cases, to solve a new prediction problem, researchers use ideas which are specific for this problem. In addition to problem-specific predictions, there exist successful prediction algorithms. Most of these algorithms are heuristic -- in the sense that they are empirically successful, but since they do not have any domain-related theoretical justification, there is no guarantee that they will work in other situations as well.;It is therefore desirable to provide more justified extrapolation algorithms -- e.g., by providing a solid justification for the existing heuristic techniques. In numerical mathematics, more justified algorithms are often called more reliable. In this thesis, we provide a theoretical justification for an important class of heuristic extrapolation algorithms -- algorithms based on partially ordered sets (posets). This justification makes these algorithms more reliable in the sense of numerical mathematics.
机译：科学与工程学的主要目标之一是预测不同情况的结果。例如，在牛顿力学中，我们希望预测未来某个时刻不同物体（例如行星）的位置和速度。在本论文中，作为案例研究，我们要预测新化学物的性质问题。物质。化学的主要目标之一是设计可用于各种实际任务的新分子（更一般而言，是新化学化合物）。新物质已经产生了用于建筑物和宇宙飞船的新材料，新炸药和新燃料，新药物等。一直在设计和测试新化合物。例如，新药物的设计方法是：合成并测试了许多不同的有前途的物质，但实际上只有极少数是有用的。合成新化合物通常是困难且耗时的。因此，需要预测新化合物的性质，以便滤除不具有所需性质的化合物。在物理学中，通常，我们知道描述感兴趣对象的精确方程，并且我们知道如何解决这些方程式。牛顿的力学就是这种情况。在这种情况下，我们将面临一个纯粹的数学问题：求解这些方程，从而根据描述给定对象的参数x1，...，xn的已知值来计算所需特性的值y。在应用领域，我们要么不知道方程式，要么方程式如此复杂以至于我们不知道如何求解它们。例如，在化学上，原则上我们可以使用量子力学方程式描述任意化学物质，但实际上，特别是对于有机物质，这些方程式太复杂而无法求解。知道方程式-或我们不知道如何求解方程式-预测问题采用以下形式：（1）我们知道一些对象a的量v（a）的值，而（2）我们知道想要为其他一些对象预测此数量的值。科学中有许多成功预测的例子。在许多情况下，为了解决新的预测问题，研究人员使用了专门针对该问题的想法。除了特定问题的预测之外，还存在成功的预测算法。这些算法大多数都是启发式的-从经验上讲它们是成功的，但是由于它们没有任何与领域相关的理论依据，因此无法保证它们也可以在其他情况下工作。提供更合理的外推算法-例如，通过为现有启发式技术提供可靠的证明。在数值数学中，更合理的算法通常被称为更可靠。在本文中，我们为一类重要的启发式外推算法（基于部分有序集（姿势）的算法）提供了理论依据。从数字数学的意义上讲，这些理由使这些算法更加可靠。

著录项

  • 作者

    Nava, Jaime.;

  • 作者单位

    The University of Texas at El Paso.;

  • 授予单位 The University of Texas at El Paso.;
  • 学科 Chemistry Analytical.;Computer Science.
  • 学位 M.S.
  • 年度 2009
  • 页码 61 p.
  • 总页数 61
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 化学;自动化技术、计算机技术;
  • 关键词

  • 入库时间 2022-08-17 11:38:29

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号