Fast-Track Consensus Study on Foundational Research Gaps and Future Directions for Digital Twins

关于数字孪生基础研究差距和未来方向的快速共识研究

• 批准号: 2233022
• 负责人: Michelle Schwalbe
• 金额: $ 9.5万
• 依托单位: National Academy of Sciences
• 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: 2022-09-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2233022&HistoricalAwards=false
• 关键词: Fast; Track; Consensus; Study; Foundational

The National Academies of Sciences, Engineering, and Medicine is undertaking a study to identify needs and opportunities to advance the mathematical, statistical, and computational foundations of digital twins in applications across science, medicine, engineering, and society. A digital twin is a computer model that changes over time to represent the structure or behavior of a unique physical entity, such as a manufacturing process, piece of equipment, or even a person. Based on data inputs, the digital twin can be used to gain insight into present and future states of the physical twin. The exploration and use of digital twins is growing across domains, but many state-of-the-art digital twins are largely the result of custom implementations that require considerable deployment resources and a high level of expertise. Due to the individualized nature of many digital twin implementations, the relative maturity of digital twins varies significantly across problem spaces. Moving from one-off digital twins to digital twin implementations at scale will involve addressing foundational mathematical, statistical, and computational gaps. This study aims to highlight these critical research gaps and provide options to address them with the goal of advancing the use of digital twins across disciplinary communities.The proposed study by the National Academies of Sciences, Engineering, and Medicine, will highlight needs and opportunities to advance the mathematical, statistical, and computational foundations of digital twins in applications across science, medicine, engineering, and society. A digital twin assimilates observational data and uses this information to continually update its internal models so that they reflect the evolving physical system. The digital twin is therefore continuously improving and provides a dynamic digital history of the physical entity. These core functionalities can be augmented with feedback control and artificial intelligence, combined with ensembles of similar twins, or used in tandem with other predictive tools to analyze and diagnose operational states and to optimize performance under real-world conditions. Utilization of digital twins varies across disciplines. This study will address the following: (1) diverging definitions of digital twins and domain-inspired use cases; (2) foundational mathematical, statistical, and computational gaps for the continued development of digital twins; (3) best practices for digital twin development and use; and (4) opportunities to move the community and state of practice forward. Three domain-specific workshops will be held to explore the methods, practices, use cases, and challenges for the development and use of digital twins—focus areas include biomedical domains, Earth and environmental systems, and aerospace engineering. Four reports will be released by the National Academies during the course of this 18-month study: three short summaries of the domain-specific workshops and a consensus report focused on the cross-cutting foundational research gaps and future directions for 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.

美国国家科学学院和医学学院正在研究，以确定应用数学，数字双胞胎在应用科学，医学，工程和社会模型中的数学和计算基础的需求和机会。甚至是基于数据输入的人，数字双胞胎也可以成为物理双胞胎的当前和未来状态。为他们提供选项的目的是促进跨学科统一的数字双胞胎的使用。国家科学院和医学学院的拟议研究将强调提高应用程序科学中的数学，统计和数字双胞胎的需求和机会，医学，工程和社会观察数据是内部模型，因此它们会撤回不断发展的物理系统。类似的双胞胎状态，并在数字双胞胎和域名的用例中优化绩效。将举行三个特定领域的研讨会，以探索用于开发和数字双胞胎的方法，实践，使用挑战，重点领域包括生物医学领域，以及在课程中将在课程中发布四个报告。在第18个月的研究中：针对特定领域的研讨会的三个简短摘要以及关于跨裁切基础研究差距的共识报告和该奖项的未来方向。审查标准。