General information
This proposal aims to develop an application-independent digital engineering methodology for creating a virtual representation (digital twin) of complex systems. By integrating multiphysics simulation tools, systems modeling languages, and diagnostic frameworks, the project will enable the coupling of subsystems through signal interfaces, parametric modeling, and behavioral analysis. A key philosophy of the project is bridging the gap between industry and research by making system-level simulation and diagnostics accessible to non-specialist engineers. To achieve this, the framework incorporates ML and natural language processing, enabling intuitive, AIdriven interaction with the simulation environment. Users will be able to describe desired operating conditions in plain language and receive accurate, data-driven analysis without requiring deep expertise in physics, mathematics, or simulation. The framework integrates AIbased preventive maintenance capabilities. By continuously tracking system health indicators through monitoring sensors and simulation feedback, the system can early signs of degradation, forecast potential failures, and recommend timely maintenance actions. This approach significantly reduces downtime, optimizes resources, and improves overall system reliability.
Co-financing organization
Business incubator
