AI-enabled digital twins are transforming government critical infrastructure

Digital twins, increasingly deployed in both public and private sector organizations, are prized for their ability to create a virtual model of a physical object or space, and for their ability to show how these objects interact with their environment. Especially as digital twins become AI-enabled, federal IT leaders are finding that these solutions help lead to better decision-making along with lowered costs and increased safety and efficiency.

Digital twins solve key challenges for government critical infrastructure

Digital twins allow an organization to dynamically model and fine-tune processes via virtual abstractions of a real-world entity and are typically informed by ongoing and often real-time data inputs. While digital twin technologies are vital for revolutionizing operations in many industries, nowhere is the need greater and the benefit more impactful than in the public and critical infrastructure sectors.

By its very nature, critical infrastructure requires high reliability and minimum downtime to support essential mission operations, whether that involves keeping production on schedule for much-needed military aircraft, maintaining power plant operations, or ensuring rapid development of an essential highway project. These models can also be used to monitor the safety of aging infrastructure or identify how city projects will impact its citizens. Digital twins enhance quality and save time by conducting design and analysis in the virtual world, making them especially useful in meeting these heightened operational demands within a government critical infrastructure setting.

The addition of artificial intelligence (AI) has further enhanced a digital twin’s value in government. AI-enabled digital twins can automatically strategize process workarounds in defense manufacturing to avoid downtime from equipment failure, streamline ER and ICU operations in a Department of Veterans Affairs hospital, or help a public health agency speed vaccine development with predictive modeling for new production lines. These are just a few examples of how AI boosts a digital twin’s capacity to support mission-critical operations through stronger performance, accuracy, scalability and predictive capabilities.

Digital twins facilitate government innovation

Given the benefits described above, it is not surprising to see that 63% of federal agencies are already investing or planning to invest in digital twins. To get the most out of the investment, federal transformation teams should carefully select the type of deployment that best fits the specific use case, with digital twin options generally broken down into three types.

A descriptive twin is an engineering design and visual representation that embodies all knowledge of a physical object or set of objects; these are especially useful for training purposes or in architectural modeling. An informative twin is similar to a descriptive twin but features an additional layer of operational and sensory data to extract performance-related insights. Finally, a predictive or autonomous twin includes updatable models that allow the digital twin to iteratively learn and take action autonomously within the organizational IT system.

Not surprisingly, government technology leaders are finding the full range of these options to be useful in both optimizing current use cases and in supporting entirely new innovations in critical infrastructure that weren’t previously possible. Consider the example of how digital twins can aid in the design of a cutting-edge fusion power generation facility:

Fusion power generation has the potential to provide near-limitless and highly sustainable energy, but developing production capabilities at scale requires intensive computational resources and artificial intelligence for development and testing. Engineering teams can achieve this by linking supercomputers with digital twin prototyping models to run the massive amounts of modeling and simulations needed for fusion research. Digital twins future-proof the development lifecycle with sensor-driven feedback loops that continually incorporate new data and metrics as technologies mature.

Key implementation priorities

For all their promise, successful digital twin deployments require government transformation teams to make the right design and configuration choices during implementation. One key priority is to ensure the correct data is being gathered and analyzed. This involves choosing the appropriate data to target, and then choosing the right design and placement of sensors and edge systems for optimal collection and analysis of that data.

Security must be a top priority during implementation to ensure seamless collaboration. Any breach in the security of a digital twin could potentially compromise the entire physical system it represents, leading to significant operational, financial and even safety risks. Therefore, thorough security measures, including data encryption, access control, authentication protocols and regular security audits are essential to safeguarding digital twins and the systems they represent from malicious attacks and unauthorized access.

Robust authentication protocols represent another key implementation priority. As mentioned earlier, any gap in security along what could be a global network of designers and suppliers poses a potential risk to project integrity or even national security. This is why a strong access management paradigm must be in place, ideally fortified with software guard extensions (SGX) that create protected enclaves for data, and trust domain extensions (TDX) that expand these enclaves to trusted third parties.

Throughout, government transformation teams looking to implement digital twins should prioritize solutions that can integrate with existing infrastructure and systems. This is necessary in government settings where funding limitations or continuity of critical infrastructure operations make legacy systems or components unavoidable. By iteratively adding compute resources strategically and cost-effectively to legacy systems, agencies can scale the digital twin deployment overtime on a realistic path toward progressively larger and more demanding use cases.

Conclusion

AI-enabled digital twins are yielding powerful benefits for government teams in charge of designing and maintaining critical infrastructure, including faster process optimization, more situational awareness and stronger predictive capabilities. Furthermore, when agencies prioritize an incremental approach that strategically incorporates legacy assets and is driven by a clear implementation plan, the outcome is a virtuous cycle of ongoing mission success for agencies and ongoing value for taxpayers and citizens.

Burnie Legette, Director of IOT and Artificial Intelligence at Intel.

Copyright © 2024 Federal News Network. All rights reserved. This website is not intended for users located within the European Economic Area.