How "digital twins" could help us predict the future | Karen Willcox
Sep 1, 2023
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Aerospace engineer Karen Willcox discusses the potential of digital twins in engineering, climate studies, and medicine. She explains how personal data collection leads to the creation of personalized dynamic representations called digital twins. Willcox explores the wide range of applications for digital twins, from aerospace engineering to medicine, and highlights the challenges in creating digital twins of complex systems. She also emphasizes the importance of combining physics-based models with machine learning and data simulation to make better predictions and decisions.
Digital twins combine personalized data and mathematical models to create dynamic simulations of physical systems in various domains.
Creating digital twins for complex systems is challenging due to multiple scales, sparse data, and the need for predictive models.
Deep dives
The Power of Digital Twins
Digital twins are highly specific, data-driven simulations of existing systems that have the potential to tackle societal problems. They combine personalized data and powerful mathematical models to create dynamic and evolving models of physical systems. Digital twins have numerous applications, including optimizing aircraft maintenance and flight operations in the airline industry, monitoring and maintaining bridges and other infrastructure, increasing energy efficiency in buildings, and guiding medical assessment and personalized treatment. However, creating digital twins for complex systems is challenging due to the scales involved and the need for predictive physics-based models.
Challenges in Creating Digital Twins
Creating digital twins of complex systems faces several challenges. These systems span multiple scales, and computational models that resolve all scales are computationally intractable. While there is abundant data available, it is often sparse, noisy, and indirect, making it insufficient for modeling complex systems alone. Additionally, even with improved sensing technology, it is crucial to incorporate predictive models to make future predictions. However, there is hope in the field of computational science, which combines physics-based models, machine learning, data assimilation, optimization, and high-performance computing to address these challenges.
Exciting Applications of Digital Twins
Digital twins have the potential to revolutionize various sectors. In space systems, digital twins can manage the health and operations of launch vehicles and satellites, as well as track and manage space debris. In the geosciences field, digital twins can aid in understanding climate change by modeling the Antarctic ice sheet and coastal areas. In medicine, digital twins can contribute to personalized medicine, such as patient-specific heart care and cancer treatment. These exciting applications showcase the possibilities digital twins offer in creating safer and more efficient engineering systems, understanding the natural world, and improving medical outcomes.
From health-tracking wearables to smartphones and beyond, data collection and computer modeling have become a ubiquitous part of everyday life. Advancements in these areas have given birth to "digital twins," or virtual models that evolve alongside real-world data. Aerospace engineer Karen Willcox explores the incredible possibilities these systems offer across engineering, climate studies and medicine, sharing how they could lead to personalized medicine, better decision-making and more.