Combining Deep Learning and Coarse Grained Simulation Methods to Study High-Dimensional NanoBiophysical Systems

结合深度学习和粗粒度模拟方法来研究高维纳米生物物理系统

• 批准号: RGPIN-2020-07145
• 负责人: deHaan, Hendrick
• 金额: $ 2.48万
• 依托单位: University of Ontario Institute of Technology
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=741624
• 关键词: Combining; Deep; Learning; Coarse; Grained

We are in the midst of an artificial intelligence (AI) revolution. Computational advances have culminated in massive strides being taken towards having computers "think". Machine learning (ML) has driven most of these advancements where computational algorithms and methodologies - most notably neural networks - are employed to identify patterns in large data sets and then, based on this training, predict outcomes when given smaller amounts of input data. In the past couple of years ML has fundamentally changed established practices from evaluating medical data to fraud detection to financial prediction. Its impact on scientific research is equally astounding. This grant application focuses on the use of ML to solve mathematical equations. While we understand that physical things and processes can be modelled with mathematical equations, finding the equations is often less challenging than actually solving them. ML provides a highly effective and powerful way to solve equations and produce meaningful answers. We intend to advance ML techniques to solve partial differential equations (PDEs). These are a class of equations that can describe complex processes and environments. PDEs can describe the flow of molecules within nanofluidic devices which have dimensions on the order of nanometers (a nm is around ten thousand times smaller than the width of human hair). They are able to isolate, characterize, and modify single biological molecules such as DNA and proteins. A primary use of these devices is in advanced medical practices such as personalized medicine where risk, diagnosis, and therapeutics are informed by one's genetic makeup. Our goal is to develop machine learning methods and techniques to significantly enhance nanofluidic device research and design. ML methods can solve systems that depend on a large number of factors (i.e., high dimensional), allowing the study of complex systems across all possible situations; this is difficult to do with other approaches. This enables us to efficiently refine current and design new devices for tasks such as identifying unique DNA strands that indicate a particular disease. While our focus is on developing these techniques for nanofluidic devices, this knowledge will translate to a vast array of other scenarios from nuclear reactor design to understanding how cream mixes with coffee. By developing ML as a powerful tool for solving PDEs, our research can also benefit computation-based research in other academic labs as well as industrial R&D. This work will help position Canadian academic research at the forefront of the ML revolution. It also will equip Canadian businesses with a powerful tool for rapid and cost efficient development of their technologies. Through training undergraduate and graduate students, this research program will produce the highly qualified personnel needed to ensure Canada remains at the leading edge of a rapidly evolving and increasingly technological global economy.

我们正处于一场人工智能（AI）革命之中。计算技术的进步最终导致了计算机在“思考”方面的巨大进步。机器学习（ML）推动了这些进步中的大部分，其中计算算法和方法-最明显的是神经网络-用于识别大型数据集中的模式，然后基于这种训练，在给定少量输入数据时预测结果。在过去的几年里，ML从根本上改变了从评估医疗数据到欺诈检测再到财务预测的既定做法。它对科学研究的影响同样令人震惊。这个拨款申请的重点是使用ML来解决数学方程。虽然我们知道物理事物和过程可以用数学方程建模，但找到方程往往比实际求解它们更具挑战性。ML提供了一种非常有效和强大的方法来求解方程并产生有意义的答案。我们打算推进ML技术来解决偏微分方程（PDE）。这是一类可以描述复杂过程和环境的方程。偏微分方程可以描述纳米流体装置内的分子流动，这些纳米流体装置具有纳米量级的尺寸（纳米大约比人类头发的宽度小一万倍）。它们能够分离、表征和修饰单个生物分子，如DNA和蛋白质。这些设备的主要用途是在先进的医疗实践中，例如个性化医疗，其中风险、诊断和治疗取决于一个人的基因构成。我们的目标是开发机器学习方法和技术，以显着提高纳米流体设备的研究和设计。ML方法可以解决依赖于大量因素的系统（即，高维），允许在所有可能的情况下研究复杂系统;这是很难用其他方法做到的。这使我们能够有效地改进现有设备并设计新设备，以完成识别指示特定疾病的独特DNA链等任务。虽然我们的重点是为纳米流体设备开发这些技术，但这些知识将转化为从核反应堆设计到了解奶油如何与咖啡混合的大量其他场景。 通过开发ML作为解决偏微分方程的强大工具，我们的研究也可以使其他学术实验室和工业研发中基于计算的研究受益。这项工作将有助于将加拿大的学术研究置于机器学习革命的最前沿。它还将为加拿大企业提供一个强大的工具，以快速和具有成本效益的方式开发其技术。通过培养本科生和研究生，该研究计划将产生所需的高素质人才，以确保加拿大在快速发展和日益技术化的全球经济中保持领先地位。