How “Digital Twins” Could Help Us Predict the Future | Karen Willcox | TED

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In this section, the speaker introduces the topic of the revolution in computing and the role of data and models in health tracking devices and smartphones.

The Revolution in Computing

• The widespread use of health tracking devices and smartphones indicates a revolution in computing over the last decade.

• These devices collect personalized data about our health, movements, and habits.

• Inside these devices are powerful mathematical and statistical models that learn from data or are based on physics.

• Data assimilation is the process of updating models with new data collected from the system, allowing for continuous evolution of personalized models.

• Personalized models enable predictions and recommendations tailored to individuals' dynamically evolving states.

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This section explores how similar revolutions have been taking place in engineering systems, where data and models play a crucial role.

Revolution in Engineering Systems

• Engineering systems also rely on data collection through sensors that have become smaller, lighter, cheaper, and more powerful.

• Models used in engineering systems are grounded in physics and allow for predicting system responses.

• Personalized models called digital twins can be created by combining data from sensors with engineering system models.

• Digital twins capture individual differences between systems and evolve as the system ages, degrades, gets damaged, or repaired.

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This section emphasizes the importance of personalization through data assimilation to create digital twins for engineering systems.

Personalization through Data Assimilation

• Data assimilation is crucial for personalizing models to specific individuals or engineering systems.

• Digital twins enable personalized predictions and recommendations based on continuously updated data from sensors.

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This section highlights the power of digital twins in decision-making for engineering systems.

Power of Digital Twins

• Digital twins are personalized, dynamically evolving models of physical systems.

• They capture individual variability and change as the system ages, degrades, gets damaged, or repaired.

• Having a digital twin for each vehicle in a fleet can greatly impact decision-making in areas such as maintenance and optimal operation.

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This section discusses the process of creating a digital twin for an aircraft and its continuous evolution through data assimilation.

Creating a Digital Twin

• Data from sensors on board an aircraft, as well as inspections, are collected to create a personalized model known as a digital twin.

• The digital twin captures the specific characteristics and differences of the individual aircraft.

• Data assimilation continually updates the digital twin as new data is collected throughout the life of the aircraft.

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This section explores the potential benefits of having digital twins for decision-making in industries like airlines or unmanned cargo delivery drones.

Benefits of Digital Twins

• Having a digital twin for each vehicle in a fleet enables informed decisions based on real-time information about their evolving states.

• Decision-making regarding maintenance and optimal operation can be tailored to each individual vehicle's needs and mission requirements.

Digital Twins: A Powerful Concept

This section introduces the concept of digital twins and their origins in the Apollo program. It highlights the potential applications of digital twins in various fields.

The Origin of Digital Twins

• The term "digital twin" was coined in 2010, but the idea of combining models and data to create personalized models is much older.

• The Apollo program in the '60s and '70s is often cited as one of the first instances where digital twins were used. NASA deployed a virtual model on the ground to follow along with missions.

Digital Twins in Action

• The Apollo 13 mission showcased the importance and usefulness of digital twins. When the spacecraft suffered a malfunction, NASA used data from the real spacecraft to feed into simulators on the ground, guiding decisions that brought astronauts back safely.

• Today, digital twins are not limited to aerospace engineering. They are being used for structural health monitoring, predictive maintenance, energy efficiency in buildings, wind farms optimization, and even creating digital twins of natural environments like forests and coastal regions.

• In medicine, there is interest in using digital twins for medical assessment, diagnosis, personalized treatment guidance, and drug testing.

Challenges in Creating Digital Twins

• Creating digital twins for complex systems like entire aircraft or cancer patients is still challenging due to multiple scales involved. Changes at microscopic levels can have impacts at system levels.

• While big data exists for many systems today, it is often sparse, noisy, and indirect when it comes to complex systems. Limited sensors or observations from outside make it difficult to fully understand what's happening inside these systems.

• Even with advancements in sensing technology, having enough data to characterize complex systems is not sufficient. Predicting future outcomes and guiding decisions require computational models.

The Power of Digital Twins

This section explores the power and potential benefits of digital twins in various domains.

Enhancing Decision-Making

• Digital twins have the potential to enhance decision-making by providing insights into system behavior, predicting outcomes, and simulating different scenarios.

• They can help optimize performance, improve efficiency, reduce downtime, and enable proactive maintenance in industries like manufacturing, energy, transportation, and healthcare.

Enabling Personalization

• Digital twins can be personalized models that capture individual characteristics or conditions. In healthcare, they can assist in personalized treatment plans based on patient-specific data.

• Personalized digital twins can also be used for virtual testing of drugs or medical interventions before applying them to real patients.

Supporting Sustainability

• By creating digital twins of infrastructure like buildings or wind farms, it becomes possible to optimize energy consumption and reduce environmental impact.

• Digital twins can aid in monitoring natural resources like forests or coastal regions to support sustainable management practices.

Current Limitations and Future Directions

This section discusses the current limitations of digital twin technology and potential future directions for advancement.

Computational Challenges

• Computational challenges arise due to the complexity of modeling multiple scales within a system. Resolving all scales from microscale to system level is computationally intractable even with supercomputing power.

Data Limitations

• Sparse, noisy, and indirect data pose challenges when creating accurate digital twins. Limited access to internal measurements in complex systems hinders complete understanding.

Advancements Needed

• Advancements in sensing technology and data collection are necessary to improve the quality and quantity of data available for digital twin creation.

• Integration of artificial intelligence (AI) and machine learning techniques can help overcome computational challenges and enhance the accuracy of digital twins.

Conclusion

This section concludes the talk by summarizing the potential of digital twins, acknowledging current limitations, and emphasizing the need for further advancements.

• Digital twins offer exciting possibilities across various domains, including engineering, science, medicine, and sustainability.

• While challenges exist in creating digital twins for complex systems, ongoing research and technological advancements hold promise for overcoming these limitations.

• The combination of computational models and data-driven insights can revolutionize decision-making processes and enable personalized solutions.

• Continued efforts in improving data quality, computational capabilities, and integration with AI will drive the future development of digital twin technology.

Predictive Physics-Based Models and Digital Twins

In this section, the speaker discusses the use of predictive physics-based models and digital twins in various fields such as cancer research, climate science, space systems, and medicine.

Predictive Physics-Based Models and Machine Learning

• Predictive physics-based models can be used to predict how a cancer tumor might grow or respond to radiotherapy treatment.

• These models can also be applied to predict how an Antarctic ice sheet might flow under different temperature scenarios.

• By combining these physics-based models with powerful machine learning techniques, scalable methods, and data simulation, more accurate predictions can be made.

The Role of Digital Twins in Computational Science

• The interdisciplinary field of computational science focuses on bringing together predictive physics-based models, machine learning, optimization and decision making, and high-performance computing.

• The Oden Institute at UT Austin brings faculty from 24 different departments across campus to tackle challenging problems in computational science.

Exciting Applications of Digital Twins

• Digital twins have the potential to make a difference in tackling some of the biggest problems facing society.

• In space systems, digital twins can help manage the health and operations of space systems including launch vehicles and satellites. They can also track and manage space objects and debris.

• In environmental sciences, digital twins can play a role in understanding complex phenomena like the behavior of the Antarctic ice sheet. They can guide decisions about drilling ice cores and inform future climate-related decision-making.

• In medicine, digital twins have applications in personalized medicine. They can be used for patient-specific care in areas such as heart care and cancer treatment.

Closing Remarks: Envisioning a Future with Digital Twins

In this section, the speaker concludes by emphasizing the potential impact of digital twins on engineering systems' safety and efficiency as well as our understanding of the natural world.

Envisioning a Future with Digital Twins

• The speaker encourages the audience to imagine a future where digital twins enable safer and more efficient engineering systems.

• Digital twins have the potential to provide a better understanding of the natural world around us.

• The speaker expresses excitement about the possibilities that digital twins offer in various fields.

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