Modeling and Visualization in Computationally-Intensive Science

利用国家科学基金会和马里兰大学的匹配资金，Frederick Suppe博士将购买一台硅胶图形UNIX科学工作站。这将使他能够分析在普林斯顿大学构造地质组长达一年的研究期间收集的数据。苏佩博士是一名科学史学家，他希望了解科学发现的过程是如何运作的。他指出，今天的许多研究不是在标准理论驱动的背景下进行的，而是通过构建模型来进行的，这些模型源于对非常大量的数据的计算机分析。通过进一步的建模，对数据进行简化、细化和处理。最终产品越来越多地以模型而不是理论的形式出现。苏佩博士指出，科学哲学对这种当代建模实践的关注相对较少。自1991年以来，苏佩博士一直在研究计算密集型科学，以及海量数据集如何改变科学的完成方式。作为参与者，他观察了普林斯顿大学的一个构造地质学小组，该小组利用极大的数据集，并使用计算机可视化技术搜索模式。在研究期间，该小组驳斥了流行的金星模型，证明金星今天在地质上是活跃的。在这项工作的过程中，苏佩博士积累了大量的电子数据，利用NSF提供的设备，他将使用这些数据追踪发现过程。这项工作很重要，因为它将为研究过程带来新的曙光。

With matching funds from the National Science Foundation and the University of Maryland, Dr. Frederick Suppe will purchase a Silicone Graphic UNIX scientific workstation. This will permit him to analyze data collected during a year-long study of Princeton University's Structural Geology group. Dr. Suppe is an historian of science and wishes to understand how the process of scientific discovery works. He has noted that today much research does not proceed in a standard theory driven context but rather proceeds through the construction of models which derive from the computer analysis of very large masses of data. Data are reduced, refined and manipulated by further modeling. The end-products are increasingly in the form of models and not theories. Dr. Suppe notes that the philosophy of science has paid relatively little attention to such contemporary modeling practices. Since 1991 Dr. Suppe has been studying computationally intensive science and how massive data sets are changing the way science is done. He has worked as a participant, observing a Princeton structural geology group which utilizes extremely large data sets and search for patterns using computer visualization techniques. During the period of study, the group refuted the prevailing models of Venus by showing that the planet is geologically active today. In the course of this work, Dr. Suppe has amassed a large set of electronic data and with the NSF provided equipment he will trace the discovery process using them. This work is important because it will shed new light on the research process.