Harnessing the Data Revolution to Enable Predictive Multi-scale Modeling across STEM

利用数据革命实现跨 STEM 的预测性多尺度建模

• 批准号: 2152014
• 负责人: Keith Promislow
• 金额: $ 296.56万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2152014&HistoricalAwards=false
• 关键词: Harnessing; Data; Revolution; Enable; Predictive

Development of lightweight materials for cars and airplanes, prediction of the weather in space and on earth, and design of new drugs for medical treatments are examples of important societal problems that require modeling of physical processes that occur at multiple and vastly different scales. For example, the external deflection of a composite airplane wing can be measured in inches but the displacements of the molecules inside that composite material, which ultimately govern the deflection, are a fraction of the thickness of a hair. With continuing advances in computers and computational modeling, it is now possible to reliably predict what happens at any of these scales. However, it is still challenging to design predictive models across multiple scales. This National Science Foundation Research Traineeship (NRT) project will facilitate research and training that combines novel artificial intelligence and machine learning techniques with traditional computer simulations and high-performance computing. The new methods will preserve physical structure from the small to the large and enable the discovery of new science and engineering applications of importance to society. The project anticipates training 100 Ph.D. students, including 35 funded trainees, from a variety of areas, including Computational Mathematics, various Engineering disciplines, Mathematics, Statistics and Probability, Biochemistry and Molecular Biology, and Physics and Astronomy. The research and training in this project are targeted at modeling multi-scale phenomena where well-formed and validated modeling hierarchies exist but where traditional modeling and simulation techniques break down. Recent years have witnessed the development of machine learning approaches and their broad impacts in computational modeling and scientific computing. Despite the overwhelming success machine learning has had in other areas such as natural language processing and image analysis, it is unlikely that modeling exclusively based on machine learning can replace traditional simulations that explicitly incorporate the domain knowledge arising from the physical mechanisms, symmetries, and constraints. This project will advance training and research in predictive modeling of multi-scale phenomena in complex fluids, biophysics, and polymeric materials by advancing the hybridization of traditional accurate, high-performance numerical methods with modern structure-preserving machine learning techniques. The project will develop a systematic approach to train a hierarchy of models that result in tractable simulations using high-fidelity computed and experimental data. These will unlock challenging problems in the application areas of the project and beyond. The project will also deliver a model for educating tomorrow’s graduate students and STEM workforce in predictive modeling of multi-scale phenomena. It will include core coursework leading to a graduate certificate, a course on scientific communication, and targeted short courses in cutting-edge topics, as well as an internship at a partner site and a rigorous professional development program. Upon leaving the program, the trainees will be ready to pursue careers in research and training in academia, industry, or national labs.The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

汽车和飞机轻质材料的开发，太空和地球天气的预测，以及医疗新药的设计，都是重要的社会问题的例子，这些问题需要对发生在多个和非常不同的尺度上的物理过程进行建模。例如，复合材料飞机机翼的外部挠度可以用英寸来测量，但最终决定挠度的是复合材料内部分子的位移，是头发厚度的一小部分。随着计算机和计算建模的不断进步，现在可以可靠地预测在任何一个尺度上发生的事情。然而，设计跨多个尺度的预测模型仍然是具有挑战性的。这个国家科学基金会研究培训(NRT)项目将促进将新型人工智能和机器学习技术与传统计算机模拟和高性能计算相结合的研究和培训。新方法将从小到大保存物理结构，并能够发现对社会具有重要意义的新科学和工程应用。该项目预计将培训100名博士生，其中包括35名受资助的学员，他们来自不同的领域，包括计算数学、各种工程学科、数学、统计和概率、生物化学和分子生物学以及物理和天文学。本项目的研究和培训的目标是对多尺度现象进行建模，在这些现象中，存在形式良好和经过验证的建模层次结构，但传统的建模和仿真技术无法使用。近年来，机器学习方法的发展及其在计算建模和科学计算中的广泛影响。尽管机器学习在自然语言处理和图像分析等其他领域取得了压倒性的成功，但完全基于机器学习的建模不太可能取代显式地结合来自物理机制、对称性和约束的领域知识的传统模拟。该项目将通过将传统的精确、高性能的数值方法与现代结构保持机器学习技术相结合，促进复杂流体、生物物理和聚合物材料中多尺度现象预测建模的培训和研究。该项目将开发一种系统的方法来训练一系列模型，这些模型使用高保真的计算和实验数据产生易于处理的模拟。这些将揭开项目应用领域和其他领域的挑战性问题。该项目还将提供一个模型，用于培训未来的研究生和STEM工作人员对多尺度现象进行预测建模。它将包括获得研究生证书的核心课程、科学交流课程、有针对性的尖端主题短期课程，以及在合作伙伴网站的实习和严格的职业发展计划。毕业后，学员将准备好在学术界、行业或国家实验室从事研究和培训工作。NSF研究培训(NRT)计划旨在鼓励开发和实施大胆的、具有潜在变革性的STEM研究生教育培训模式。该计划致力于通过创新的、基于证据的、与不断变化的劳动力和研究需求保持一致的综合实习生模式，在高度优先的跨学科或趋同研究领域对STEM研究生进行有效培训。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。