Conference: Mathematical Opportunities in Digital Twins (MATH-DT)

会议：数字孪生中的数学机会 (MATH-DT)

• 批准号: 2330895
• 负责人: Harbir Antil
• 金额: $ 9.99万
• 依托单位: George Mason University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2330895&HistoricalAwards=false
• 关键词: Conference; Mathematical; Opportunities; Digital; Twins

Recent advances in physics-based modeling, data-science, sensor technology, and computational mathematics have made it feasible in many areas to produce a 'Digital Twin' of a complex real world system. Such twins have shown significant promise to better understand, monitor, predict, and control real-world systems, particularly in cases where not every aspect about the system can be observed or modeled. This can improve safety, comfort, maintenance and, also the health and well-being of humans. However, many fundamental questions and challenges remain, particularly regarding a rigorous (mathematical) foundation for this emerging field. This award provides support for a workshop titled “Mathematical Opportunities in Digital Twins” to be held on December 11-13, 2023, in the George Mason University's campus in Arlington, VA. The workshop brings together key experts working in many aspects of mathematics, key application fields, and industry with the goal to determine the ways in which mathematics can contribute to the research on Digital Twins and how Digital Twins can open up new mathematical directions, as well as to identify connections, synergies, and organizational efforts within the mathematical community, and to/with other disciplines. Digital Twins can lead to new developments in many applications, such as: engineering by e.g., determining weaknesses in structures such as bridges, nuclear plants, or wind turbines; medicine, where Digital Twins of organs may lead to better cures and understanding; society, where Digital Twins of large-scale events like sport games can improve safety. A broad impact of the conference is facilitated by the conference website featuring videos and slides of talks and a technical report that will be shared with the entire scientific community. Students and early career researchers are invited to the workshop, with special attention given to groups traditionally underrepresented in STEM. Mathematical models and computations have always played a significant role in simulating, understanding, and predicting physical phenomena. While traditionally, many models have been based on first principles via a rigorous mathematical foundation, such approaches face limitations: Not everything in a physical system can be captured using physical principles, and the available computing resources and algorithms may be unable to model an entire complex system, particularly in real-time environments. Significant advances in sensing technology have enabled the equipment of complex real-world systems with sensors and to employ the sensor data to inform the workings of the system. Moreover, recent developments in data science and machine learning have strengthened the interest and confidence in empirical methodologies. However, purely empirical approaches do not take advantage of the physical principles and may require measurements and data generation at a cost that is not feasible. This workshop focuses on 'Digital Twins', which aim to combine physics-based models with data-driven models, with the goal to leverage the best of both worlds. Digital Twins bring together several research areas in mathematics (including: modeling, analysis, control, optimization, numerical analysis, and scientific computing). This workshop is expected to stimulate new developments in these important areas and to initiate new collaborations, and strengthen the existing ones, among the researchers with diverse background on mathematics of Digital Twins.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.

基于物理的建模、数据科学、传感器技术和计算数学的最新进展使得在许多领域生产复杂现实世界系统的“数字孪生”变得可行，这种孪生在更好地理解和监控方面表现出了巨大的希望。 、预测和控制现实世界的系统，特别是在系统的每个方面都不能被观察或建模的情况下，这可以提高安全性、舒适性、维护性以及人类的健康和福祉。问题和挑战依然存在，特别是在严格的该奖项为将于 2023 年 12 月 11 日至 13 日在弗吉尼亚州阿灵顿乔治梅森大学校园举办的题为“数字孪生中的数学机会”的研讨会提供支持。从事数学、关键应用领域和行业许多方面工作的主要专家，其目标是确定数学如何为数字孪生研究做出贡献以及数字孪生如何开辟新的数学方向，例如以及确定数学界内以及与其他学科的联系、协同作用和组织努力，数字孪生可以在确定许多应用方面带来新的发展，例如：通过桥梁、核等结构的弱点进行工程。植物或风力涡轮机；医学，器官的数字双胞胎可以带来更好的治疗和理解；社会，体育比赛等大型活动的数字双胞胎可以提高安全性。包含视频和演讲幻灯片和技术报告将与整个科学界分享，并邀请学生和早期职业研究人员参加研讨会，特别关注传统上在 STEM 领域代表性不足的群体。传统上，许多模型都是通过严格的数学基础基于第一原理来模拟、理解和预测物理现象，但此类方法面临局限性：并非物理系统中的所有内容都可以使用物理原理以及可用的计算资源和数据来捕获。算法可能无法建模整个复杂系统，特别是在实时环境中，传感技术的重大进步使得复杂的现实世界系统能够配备传感器，并利用传感器数据来通知系统的工作。此外，数据科学的最新发展。和机器学习增强了人们对实证方法的兴趣和信心。然而，纯粹的实证方法并没有利用物理原理，并且可能需要以不可行的成本进行测量和数据生成。旨在将基于物理的模型与数据驱动模型，旨在利用数字孪生的优势，汇集数学领域的多个研究领域（包括：建模、分析、控制、优化、数值分析和科学计算）。数字孪生数学领域具有不同背景的研究人员之间开展新的合作，并加强现有的合作。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值进行评估，认为值得支持以及更广泛的影响审查标准。