Ted Talks Daily – How “digital twins” could help us predict the future | Karen Willcox

Intro

In this episode of Ted Talks Daily, Karen Willcox discusses the concept of “digital twins” and how they can help us predict the future. She explains how these data-driven simulations of existing systems can be used to tackle societal problems, personalize models, and make optimal decisions. Willcox also explores the challenges and potential applications of digital twins in various industries.

Main Takeaways

Advantages of Digital Twins

• Digital twins are data-driven, highly specific digital simulations of existing systems.
• They can be used to tackle the biggest societal problems of our time.
• Personalized data and models can be put together through data assimilation to create personalized models that evolve with the system.
• This personalized model can then be used for predictions and recommendations tailored to the individual.

Role of Computing and Mathematical Models

• The revolution in computing has led to the collection of personalized data and the use of powerful mathematical and statistical models in devices such as health tracking devices and smartphones.
• Powerful mathematical and statistical models can predict how engineering systems will respond.
• Personalized models of engineering systems are called digital twins.
• Digital twins are dynamically evolving models of physical systems that capture differences and variability.

Applications and Challenges

• Digital twins can be used to make optimal decisions about maintenance and operation of a fleet of vehicles.
• Digital twins have been used since the Apollo program and are now being used in various industries beyond aerospace engineering.
• Digital twins are being used to increase efficiency and reduce downtime in wind farms, and are also being explored for various other applications such as forests, farms, ice sheets, coastal regions, oil reservoirs, and medical purposes.
• Creating digital twins of complex systems such as aircraft, cancer patients, and planet Earth is still beyond reach due to the challenges of crossing scales and sparse, noisy, and indirect data.

Predictive Physics-Based Models and Machine Learning

• Sensing technology may improve in the future, but models will still be necessary to predict future outcomes.
• Predictive physics-based models that encode the governing laws of nature can be combined with machine learning, data simulation, optimization, decision-making, and high-performance computing to address the challenge of creating digital twins of complex systems.
• “Bringing these predictive physics-based models together with powerful machine learning, with scalable methods in data simulation and optimization and decision-making, and with high-performance computing, that’s the realm of” (speaker not named).

Potential Applications of Digital Twins

• Digital twins have a role to play in managing the health and operations of space systems and tracking space debris, as well as in understanding the environment and geosciences.
• Digital twins can inform decision making around our future climate, such as where to drill ice cores and take observations.
• In medicine, digital twins can realize the promise of personalized medicine, such as patient-specific heart care and building digital twins for cancer patients.
• A future world where digital twins are enabling safer, more efficient engineering systems, a better understanding of nature, and better medical outcomes is exciting and possible.

Summary

Digital Twins: Predicting the Future

Digital twins are highly specific digital simulations of existing systems that can be used to tackle societal problems. By combining personalized data and models through data assimilation, personalized models that evolve with the system can be created. These personalized models enable predictions and recommendations tailored to the individual, leading to more efficient decision making.

Advancements in Computing and Mathematical Models

The revolution in computing has allowed for the collection of personalized data and the use of powerful mathematical and statistical models. These models can predict how engineering systems will respond, and personalized models of engineering systems are known as digital twins. These dynamically evolving models capture differences and variability, providing a comprehensive understanding of the system.

Challenges and Potential Applications

While digital twins have been used since the Apollo program, creating digital twins of complex systems such as aircraft, cancer patients, and planet Earth remains challenging. The main obstacles are the difficulties of crossing scales and dealing with sparse, noisy, and indirect data. However, digital twins have already proven their value in various industries beyond aerospace engineering, including wind farms, forests, farms, ice sheets, coastal regions, oil reservoirs, and medical purposes.

Combining Predictive Models and Machine Learning

Even with advancements in sensing technology, models will continue to play a crucial role in predicting future outcomes. Predictive physics-based models that encode the governing laws of nature can be combined with machine learning, data simulation, optimization, decision-making, and high-performance computing to overcome the challenges of creating digital twins of complex systems. This interdisciplinary approach holds great potential for advancing the field.

Promising Applications of Digital Twins

Digital twins have a wide range of potential applications. They can be used to manage the health and operations of space systems, track space debris, understand the environment and geosciences, and inform decision making related to our future climate. In medicine, digital twins offer the promise of personalized care, enabling patient-specific heart care and the development of digital twins for cancer patients. The possibilities for a future world powered by digital twins are exciting and within reach.

Conclusion

Digital twins have the potential to revolutionize various industries and address complex societal problems. By combining personalized data, powerful mathematical models, and advancements in computing, we can create dynamic simulations that accurately predict the future. While challenges remain, the interdisciplinary field of computational science is actively working towards overcoming them. A future where digital twins enable safer engineering systems, a better understanding of nature, and improved medical outcomes is within our grasp.