AIRES 4’s focus on the foundations of AI in digital engineering will include technical discussions in three broad tracks:
- Assured Digital Twins: This track focuses on the technical challenges associated with developing and using robust digital models, such as:
- Integrating physics or other knowledge into machine learning
- Methods for multi-scale prediction (especially multi-scale time-series prediction)
- Scaling issues associated with large models
- AI algorithms for control of complex systems
- Uncertainty quantification
- Assurance, including causal inference, explainability, and interpretability
- Verification, validation, and calibration
- Digital Twin Model Development and Lifecycle Management: This track focuses on the ecosystem and methods necessary for practical use of digital twins, such as:
- Real-time data integration, online and offline continuous learning on edge-based systems
- Edge deployment for real-time and power efficient deployment of digital twins, and integrating HPC and edge systems
- Federated learning for privacy or for data reduction
- Security and resilience
- Human-machine interface design
- Interoperability of digital twins
- Standards needs, and the use of digital twin technologies in standards
- Sensors and Data Management: This track focuses on all aspects of data relevant to digital twin technology, including
- AI supporting data acquisition, communication, data management, and data validation
- Novel experimental in-process and post-process characterization of systems
- Techniques for optimizing sensor selection and placement for digital twins
- Detecting and dealing with data bias
- Standards for data for digital twin technologies
