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都可以生产复杂的现实世界系统的“数字双胞胎”。这样的双胞胎已经表现出巨大的希望，可以更好地理解，监视，预测和控制现实世界中的系统，尤其是在观察或建模并非每个方面的情况下。这可以提高安全性，舒适性，维护以及人类的健康和福祉。但是，仍然存在许多基本问题和挑战，特别是关于这个新兴领域的严格（数学）基础。该奖项为2023年12月11日至13日在弗吉尼亚州阿灵顿的乔治·梅森大学校园举行的题为“数字双胞胎数学机会”的研讨会提供了支持。该研讨会汇集了在数学，关键应用领域和行业许多方面工作的关键专家，以确定数学可以为数字双胞胎的研究做出贡献的方式以及数字双胞胎如何打开新的数学方向，以及如何在数学社区和其他学科和其他学科和其他学科中确定联系，协同和组织努力。数字双胞胎可以在许多应用中导致新的发展，例如：例如：确定桥梁，核电站或风力涡轮机等结构中的弱点；医学，数字双胞胎器官可能会导致更好的治愈和理解；社会，在这里，数字双胞胎（例如运动游戏）可以提高安全性。会议的广泛影响是由会议网站编写的，其中包含视频和演讲幻灯片以及将与整个科学界共享的技术报告。学生和早期的职业研究人员被邀请参加研讨会，并特别注意STEM中代表性不足的团体。数学模型和计算在模拟，理解和预测物理现象方面始终发挥重要作用。尽管传统上，许多模型一直基于第一原理，但通过严格的数学基础，这种方法面临局限性：并非可以使用物理原理捕获物理系统中的所有内容，并且可用的计算资源和算法可能无法建模整个复杂系统，尤其是在实时环境中。灵敏度技术的重大进步使使用传感器的复杂现实世界系统的设备能够使用传感器数据来告知系统的工作。此外，数据科学和机器学习的最新发展增强了对经验方法的兴趣和信心。但是，纯粹的经验方法不会利用物理原则，可能需要以不可行的成本进行测量和数据生成。该研讨会的重点是“数字双胞胎”，该研讨会旨在将基于物理的模型与数据驱动的模型结合起来，并旨在利用两全其美的领域。数字双胞胎汇集了数学研究领域（包括：建模，分析，控制，优化，数值分析和科学计算）。预计该研讨会将刺激这些重要领域的新发展，并在具有数字双胞胎数学数学背景的潜水员背景的研究人员中启动新的合作并加强现有的合作。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子和更广泛的影响审查审查的审查标准来通过评估来诚实的支持。