AI and ML Methods in Verification and Validation:
Operationalizing Advanced Concepts Through Digital Twin Technologies

A Digital Twin is a dynamic, virtual representation of an operational environment that integrates real-time data, AI-driven analytics, and adaptive ML models. Digital twins provide a holistic framework for testing, monitoring, and optimizing system performance. The fidelity of the virtualized environment can be dialed up or down based on specific needs. It dynamically incorporates human-in-the-loop and machine-in-the-loop processes aligned with V&V objectives.

Dynamic Digital Twin Environment of the National Airspace System

The core advantages of Digital Twin technologies that provide dynamic, virtual representations of an operational environment include:

• Real-Time and Fast-Time Data Processing: Monitoring and analysis for enhanced situational awareness and decision-making capabilities.
• Predictive and Prescriptive Analytics: AI-driven insights provide prescriptive recommendations to optimize decision-making and reduce operational risks.
• Scenario Simulation and Risk-Free Experimentation: Extensive scenario testing, for risk reducing “what-if” analyses, to evaluate new technologies, advanced concepts, and emergency response plans in a safe, controlled environment.
• Automation and Adaptive Learning: AI and ML-driven self-optimizing agents refine their capabilities over time, reducing manual intervention and enhancing operational realism and accuracy of predictions.
• Complex System Optimization: AI-powered models balance multiple operational factors, such as safety, efficiency, capacity, and security, for improved system performance, resilience, and implementation outcomes.

A critical aspect of digital twins is the integration of AI-driven intelligent agents capable of simulating real-world operations within virtual environments. These intelligent agents contribute to enhanced testing and development by:

• Learning from Real-World Data: AI-driven models use reinforcement learning, supervised corrections, and adaptive behavior modeling to replicate real-world operational conditions with realistic high fidelity.
• Enabling Safe and Scalable Testing of Operational Concepts: Virtual environments allow stakeholders to evaluate strategies before deployment, mitigating risks and reducing the need for costly post-development real-world trials.
• Improving System Adaptability: AI-powered agents continuously refine their behavior and decision-making processes based on new data and evolving operational requirements.
• Simulating Human-AI Collaboration: Enabling valuable exploration of human-AI interactions, ensuring that AI-driven systems operate effectively alongside human operators.

Verifying, validating, and accrediting AI intelligent agents for digital twins involves defining performance metrics—such as accuracy, safety, transparency, and adaptability—and ensuring these are met through both automated and manual testing methods. Rigorous evaluations, including simulations, limited-scale real-world trials, and failure mode analysis, are used to verify reliability while also validating the agents’ behaviors and decision-making processes. Continuous monitoring, feedback loops, and alignment with established industry frameworks support ongoing improvement and trustworthy integration into digital twin environments.

As future advanced systems, vehicles, and devices become more AI-capable and operate increasingly autonomously, our methods, tools, and processes will be required to be correspondingly as innovative. For safety and security critical systems, innovative V&V methods will be essential to fully trust and understand emergent behaviors in operational environments. It is inconceivable that AI-capable and autonomous systems could enter operations without sufficient V&V. The safety, security, political, and financial risks would be too great. In addition, regulation and certification requirements would be extremely difficult to reliably develop and maintain.

“V&V and T&E capability technologies must be at least as advanced
as the capability being acquired—if not more so.”

Intelligent digital twins are widely used in industry and have been rapidly evolving in their application over the past few decades. Using AI and ML powered digital twins to understand, test, and operationalize AI-capable systems and autonomous vehicles involves several interconnected layers.

1. Data Collection & Processing:

• IoT Sensors & Data Streams: AI-powered digital twins may rely heavily on data from sources like sensors, cameras, and other devices within the physical system.
• Data Preprocessing & Storage: The collected raw data needs to be preprocessed, cleaned, and stored in a structured format.
• Data Analysis & Feature Extraction: AI algorithms are applied to extract meaningful features and patterns from the data, forming the basis for model training.

2. AI/ML Model Development & Training:

• Algorithm Selection: Appropriate AI and ML algorithms are chosen based on specific applications, such as regression models for predicting system behavior or anomaly detection.
• Model Training: The selected algorithms are trained using historical data or simulated data to learn underlying relationships and operational patterns.
• Model Validation & Tuning: The trained models are rigorously validated using different datasets to ensure accuracy and robustness.

3. Simulation & Execution (Virtual Twin):

• Virtual Representation: The AI/ML models may be incorporated into a digital twin (a virtual replica of the physical system).
• Scenario Simulation: The digital twin can simulate a range of scenarios, including various operational conditions, potential failures, and different experimental controls.
• Performance Analysis & Optimization: By running simulations, the digital twin helps analyze system performance, identify bottlenecks, and optimize operational parameters.
• Autonomous Control & Decision-Making: In fully autonomous systems, the digital twin can make real-time decisions and control actions based on the AI/ML models and system states.

4. Operationalization & Feedback Loop:

• Deployment: The tested and optimized AI-powered digital twin operates under real-world conditions, continuously monitoring performance and collecting data.
• Feedback & Iteration: Data from the operations is used to refine and update the AI/ML models and the digital twin itself, creating a continuous feedback loop for improvement.
• Continuous Learning & Adaptation: The digital twin learns from new data and adapts its behavior in response to changing conditions, further improving its performance and reliability.

Given the extensive resources required to develop and sustain Digital Twin environments, collaboration among government agencies, industry leaders, and research institutions is essential. To this end, a Digital Twin and AI/ML Government (DAG) working group should be established and maintained as a “force multiplier” for both government and industry, enabling accelerated progress in Digital Twin technologies to meet mission needs and deliver mutually beneficial outcomes. The proposed DAG would serve as a centralized entity dedicated to advancing and applying Digital Twin technologies in critical domains such as aviation, defense, and transportation.

“If you want to go fast, go alone. If you want to go far, go together.”
— African Proverb

The objectives of the DAG working group include:

• Accelerating AI/ML Learning Across Agencies: Facilitating the exchange of research, case studies, and best practices to enhance AI/ML capabilities for digital twins.
• Standardizing AI/ML Tools and Platforms for Digital Twins: Establishing common frameworks, tools, and methodologies to ensure interoperability and consistency across various agencies and industries.
• Facilitating Accreditation and Certification Processes: Developing standards and regulatory guidelines to support the accreditation of AI-driven digital twin technologies in security- and safety-critical systems.
• Reducing Cost Burdens Through Resource Pooling: Consolidating investments and expertise to reduce individual agency costs and maximize the impact of digital twin research and development.
• Driving Cross-Agency and Industry Collaboration: Encouraging partnerships between public and private sectors to accelerate innovation and adoption of digital twin technologies.

The DAG working group should consult and collaborate with the well-established Digital Twin Consortium® (DTC). The DTC is a global, collaborative organization dedicated to accelerating the adoption, development, and interoperability of digital twin technologies (digitaltwinconsortium.org). Established under the Object Management Group® (OMG), a not-for-profit 501(c)(6) trade association, the DTC brings together industry leaders, government agencies, and academic institutions to shape the future of digital twins. It leverages its members’ innovation and leadership to drive continuous growth across the digital engineering landscape.

The DTC’s mission is to promote awareness, adoption, interoperability, and development of digital twin technology. Through relevant guidance, reference implementations, and high-quality open-source software, the DTC enables organizations to innovate across the digital engineering lifecycle, leading to more efficient operations, enhanced decision-making, and improved product performance.

In addition, the DTC establishes liaisons with global technology associations and standards bodies that support digital twins and related disciplines across the digital engineering ecosystem. These collaborations enable the DTC to explore synergies in domain expertise, identify new use cases, advance best practices and standards, and drive progress across the emerging marketplace.

ITEA was recently established as a DTC Liaison through a signed Memorandum of Understanding (MOU), formalizing a cooperative agreement to jointly contribute to the creation and development of digital twin technology. The overarching goal of this partnership is to accelerate the development, adoption, and monetization of digital twin technology within the digital economy by preventing fragmentation and harmonizing efforts toward mutually beneficial objectives.

The integration of AI and digital twin technologies represents a transformative step in V&V methodologies for advanced transportation systems. By leveraging AI-powered digital twins, stakeholders can reduce lifecycle costs, improve operational decision-making, and foster early-stage validation of new concepts. Establishing a DAG working group, in collaboration with the already established DTC, will accelerate progress by fostering collaboration, standardizing methodologies, and optimizing resources across agencies and industries. The ability to create dynamic, data-driven virtual environments for experimentation and validation will ensure safer, more efficient, and adaptive operational landscapes. As AI and ML capabilities continue to evolve, digital twins will become indispensable in shaping the future of transportation, security, and autonomous system development. Through collaboration and innovation, we can overcome the challenges of tomorrow’s complex operational environments and build a more resilient, intelligent, safe, secure, and efficient infrastructure: turning tomorrow’s vision into reality.

Editor’s Note: This technical note is provided solely for informational purposes. It outlines preliminary concepts for consideration. The strategies and methodologies described herein remain subject to further evaluation and refinement by relevant industry experts and stakeholders.

1. Understanding AND Utilizing Digital Twins for NAS Research, David M Tuomey and Elisabeth Kim, The MITRE Corporation, April 2022, https://www.mitre.org/news-insights/publication/understanding-and-utilizing-digital-twins-nas-research

2. Intelligent digital twins and the development and management of complex systems, Michael Grieves, May 2022, https://digitaltwin1.org/articles/2-8

3. Test and Evaluation Challenges in Artificial Intelligence–Enabled Systems for the Department of the Air Force, National Academy of Sciences, 2023, https://nap.nationalacademies.org/catalog/27092/test-and-evaluation-challenges-in-artificial-intelligence-enabled-systems-for-the-department-of-the-air-force

4. What Is a Digital Twin?, NVIDIA, April 2024, https://blogs.nvidia.com/blog/what-is-a-digital-twin/