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Rare Events via Parallel Forward Flux Sampling

通过并行正向通量采样的罕见事件

基本信息

批准号：
EP/I030298/1
负责人：
Rosalind Allen
金额：
$ 31.28万
依托单位：
University of Edinburgh
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2011
资助国家：
英国
起止时间：
2011 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FI030298%2F1
关键词：
Rare Events via Parallel Forward

项目摘要

Rare events can have extremely important consequences. A recent example is of course the credit crunch of 2008, a global event of a size not seen since the 1930s. A part of the systematic failure to predict this event has been attributed to the failure of the banks' computer models to account properly for these events which, while having very low probability, are of very high import.From the scientific perspective, such a failure is understandable. Simulating rare events is extremely hard. This is for the simple reason that any given computer simulation will simply not see any rare events in any reasonable time. If you are looking for them, most (if not all) of your computational time and effort will be wasted.In our work, we will attempt to improve the situation. To do this, we will combine a powerful new technique for capturing rare events with the use of large supercomputers. The goal of the work is to provide software which will make it easier for scientists to apply this technique (known as Forward Flux Sampling ) to more difficult problems. As an example, the method can be used to study conformational (shape) changes in proteins; rare mistakes in the folding of proteins is thought to play an important role in neurodegenerative diseases. Our new software will help to make the study of such problems more straightforward. It may even help to predict the next financial crisis!

罕见的事件可能会产生极其重要的后果。最近的一个例子当然是2008年的信贷紧缩，这是自20世纪30年代以来从未有过的全球性事件。银行未能系统性地预测这一事件，部分原因是银行的计算机模型未能适当地解释这些事件，这些事件虽然概率很低，但意义非常重大，从科学的角度来看，这种失败是可以理解的。模拟罕见事件是非常困难的。这是因为任何给定的计算机模拟在任何合理的时间内都不会看到任何罕见的事件。如果你正在寻找它们，你的大部分（如果不是全部）计算时间和精力将被浪费。在我们的工作中，我们将试图改善这种情况。为了做到这一点，我们将联合收割机与大型超级计算机结合使用一种强大的捕捉罕见事件的新技术。这项工作的目标是提供软件，使科学家更容易将这种技术（称为前向通量采样）应用于更困难的问题。例如，该方法可用于研究蛋白质的构象（形状）变化;蛋白质折叠中的罕见错误被认为在神经退行性疾病中起重要作用。我们的新软件将有助于使这类问题的研究更加直接。它甚至可以帮助预测下一次金融危机！