arXiv:2605.28684v1 Announce Type: new Abstract: Reduced-order models (ROMs) can accelerate high-dimensional dynamical simulations, but their accuracy often deteriorates when online dynamics leave the regime represented by offline training data. We develop a projection-based adaptive ROM framework based on incremental singular value decomposition (iSVD), in which occasional full-order operator evaluations provide correction snapshots for online basis updates. The intrusive ROMs considered here are fully parameterized by the basis, so each update naturally propagates to reduced operators and hyp
This research addresses a key limitation in current reduced-order models by enabling online adaptation, a critical capability for deploying these models in dynamic, real-world AI applications.
Adaptive reduced-order models (ROMs) enhance the reliability and efficiency of high-dimensional simulations, which are foundational for complex AI systems and scientific computing across various sectors.
The ability to dynamically update ROMs online makes them more robust and applicable to situations where system dynamics evolve, reducing the need for extensive retraining and improving real-time performance.
- · AI/ML developers
- · Scientific computing sector
- · Aerospace and automotive simulation
- · High-performance computing
- · Traditional fixed-basis ROM approaches
- · Entities reliant on highly static simulation environments
More accurate and efficient AI-driven simulations in fields like engineering and climate modeling become feasible.
Reduced computational costs and faster iteration cycles for complex system design and optimization.
Acceleration of autonomous system development and deployment requiring real-time, adaptive predictive capabilities.
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Read at arXiv cs.LG