arXiv:2606.16219v1 Announce Type: cross Abstract: Digital twin modeling, including control and data assimilation under model uncertainty, often faces an open-ended fidelity problem: adding variables, data streams, and time scales can indefinitely increase model complexity, ultimately producing systems that are difficult to maintain, validate, interpret, and use for stress or safety testing. As an alternative, one can seek parsimonious stochastic surrogate models built only on the variables needed to describe the relevant quantities of interest. We introduce a framework for discovering such var
The increasing complexity of digital twins and the computational demands of AI models necessitate more efficient and interpretable modeling approaches, pushing research towards parsimonious surrogate models.
This development can significantly enhance the development and application of digital twins across various industries, making them more robust, maintainable, and verifiable for critical applications.
The methodology for constructing and managing complex digital twin models shifts towards a more focused, stochastic, and interpretable approach, prioritizing relevant quantities of interest over comprehensive replication.
- · Aerospace & Defense
- · Manufacturing
- · Energy Sector
- · AI/ML researchers
- · Developers of overly complex digital twins
- · Traditional simulation software companies
More efficient and reliable digital twins for design, performance optimization, and risk assessment will become possible.
Accelerated innovation cycles in industries heavily reliant on simulation and digital prototyping, leading to faster product development and deployment.
The integration of these advanced digital twins with real-time data streams could create a new paradigm for autonomous control and self-optimizing systems in critical infrastructure.
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