The Digital Twin Consortium® (DTC) announced it added eight new testbeds to its Digital Twin Testbed Program, bringing the total to 16. Members can model, simulate, integrate, verify, deploy, and optimize digital twin solutions by providing unprecedented access to early-stage testbed development.
“We’re excited to announce these innovative digital twin testbeds,” said Dan Isaacs, GM & CTO of the DTC. “We’re seeing strong interest from members worldwide in participating in our collaborative testbed program. Our members already utilize this program to develop and adopt AI-powered intelligent digital twins, generative AI digital twins, and other enabling technologies, advancing the core technologies that drive tomorrow’s digital transformation.”
DTC’s eight new member-led testbeds include:
- TWINSENSE: AI-based Virtual Sensing for Enhanced Real-time Understanding and Learning Systems Enhancement – shows how digital twin technology can perform real-time virtual measurements of critical variables across diverse industrial assets. It addresses the challenge of measuring inaccessible or costly-to-monitor variables, leveraging digital twins for continuous virtual sensing. The testbed also calibrates AI-based novelty detection systems using transfer learning techniques that combine virtual and real-world data, enabling AI-driven proactive maintenance and improving maintenance accuracy by 40%. Lead Developer: Aingura IIoT, Co-Developer: XMPro
- AEGIS: Agent-Empowered Guidance for Improving Student Outcomes – The testbed shows that multi-agent systems, trained on survey data from high-risk students, can identify cognitive-emotional triggers that impact learning efficacy. The testbed simulates intervention scenarios and demonstrates how students can be trained to respond more effectively to these triggers, leading to improved engagement and reduced dropout rates. It validates AI-powered interventions for personalized learning and dropout prevention in education. Lead Developer: My Performance Learning, Co-Developer: Crysp
- FAB – Factory-in-a-Box for Rapid Disaster Manufacturing – The testbed is a mobile, modular digital twin-enabled manufacturing unit that can produce critical energy components in disaster-struck zones. It reduces transport costs and logistics burden, minimizes downtime of essential systems and infrastructure, and provides localized, resilient production with minimal setup. It also enables remote coordination through a digital twin interface. Field-deployable, these production systems improve community resilience and demonstrate the feasibility of digital twin-enabled micro-manufacturing in high-stress scenarios. Lead Developers: DRG Solutions and Oak Ridge High School, Contributing Technology Providers: Oak Ridge National Laboratory.
- Q-Smart: Quantum Secure Data Exchange for Resilient Smart Home Cognitive Networks – The Q-Smart testbed validates a cognitive, secure, self-learning platform-independent intelligent home system built on decentralized open-source components. It creates a personal cognitive hub using wireless mesh networks, dynamic live 3D models (digital twins), multi-agentic AI frameworks, and XR interfaces for energy optimization and indoor air quality management. The system emphasizes edge-native processing, ensuring all data remains within the home while leveraging quantum-safe (PQC-ready) protocols for future-proof security. By focusing on self-learning algorithms, it predicts and controls home aspects like HVAC and ventilation, reducing energy consumption by up to 25% and enhancing occupant comfort. Lead Developer: WINNIO.
- TRANSFORM -The TRANSFORM testbed validates an application framework that systematically converts static 2D data schemas into dynamic 4D geospatial representations withreal-time updates. The testbed addresses the critical challenge of standardized data interoperability across multiple applications while maintaining 99.9% data integrity during transformation. Using smart city infrastructure as the primary validation environment, the framework demonstrates seamless data transformation across transportation, utilities, and emergency services applications. Lead Developer: EDX Technologies, Co-Developer: Crysp.
- SAFESME: Smart Asset Fast Enablement for SME Equipment—The testbed demonstrates digital twin–driven commissioning and digital service enablement for SME manufacturing equipment, specifically injection molding machines and packaging machines. It validates that SME-scale manufacturing equipment can achieve cost-effective digital twin onboarding and digital service transformation. The testbed enables rapid, automated onboarding and commissioning in under 5 minutes per asset, reduces setup time and operator effort, and maintains high model alignment and API performance, all without requiring expensive PLC upgrades or high development overhead. Lead Developer: HS Soft.
- Early Notification & Guidance for Academic Growth & Engagement (ENGAGE) – The testbed is focused on determining whether a digital twin can be used to identify and support at-risk students. The testbed will create a comprehensive digital twin system that integrates academic scores, class participation, extracurricular involvement, behavioral indicators, and sentiment analysis to surface previously unmeasured emotional and engagement signals that are critical for student retention but currently invisible in traditional monitoring systems. Lead Developer: Austin Community College District.
- Synthetic Healthcare Pathway Digital Twin (SYNTHEKID) – The testbed transforms regional healthcare delivery through an innovative synthetic digital twin that models chronic kidney disease (CKD) pathways across Yorkshire, UK. It validates how privacy-preserving digital twins can optimize healthcare systems, enabling scenario planning and demand forecasting without compromising patient confidentiality. The platform validates critical intervention points that can improve outcomes and system efficiency by simulating patient journeys from early detection to clinical progression. Lead Developer: Health Innovation Network Yorkshire and Humber; Co-Developers: Nexus, Counterpoint Technologies, Crysp.
The Digital Twin Testbed Program implements DTC’s Composability Framework—utilizing the Business Maturity Model, Platform Stack Architecture, and Capabilities Periodic Table—alongside a capabilities-focused maturity assessment framework that incorporates the evaluation of Generative AI, multi-agent systems, and other advanced technologies.
