Effective utilization of design models for Digital Twins and PHM


  • Interpretability, explainability and trust related to inferences produced by AI-based digital twins (DTs)
  • Ability to incorporate domain and physics-based constraints into DT
  • General robustness of model in presence of noise and other sources of variations in the input
  • Adversarial robustness of model, and safety related to its application, e.g., adversarial perturbations in the input to fool model inference
  • Optimal decision support arising from inference: allocation of human v/s machine decision making, design of human-in-the-loop systems
  • In the context of Control systems, defensible linearization of DT output to enable ease of application in PID loops
  • Validation and verification process of Digital Twin model so outcomes can be proved to be safe and assured
  • Lack of labels to permit fully supervised training of DTs
  • Scalable approach for continuous learning and management of DTs over time
  • Need for scalable Federated/non-centralized learning in cases where data cannot be made centrally available due to various reasons
  • Manage a network of DTs that interact and participate in a larger industrial ecosystem: scalable uncertainty propagation and management in system-of-systems network
  • Scalable DTs that can operate on the Edge with high performance and low footprint
  • DTs for reliable long-term forecasting (months look ahead)