arXiv:2605.04130v2 Announce Type: replace Abstract: High-fidelity simulations, such as computational fluid dynamics and finite element analysis, are essential for modeling complex engineering systems but are often prohibitively expensive for tasks including parametric studies, optimization, and real-time control. Projection-based reduced-order models (ROMs) alleviate this cost by projecting the governing dynamics onto low-dimensional subspaces. However, their performance can deteriorate under parameter variation, motivating the need for adaptive basis construction. In this work, we propose a c
The increasing complexity and computational cost of high-fidelity simulations are driving a demand for more efficient modeling techniques, especially with the advancement of AI methods.
This development allows for more accurate and adaptable reduced-order models, which can significantly accelerate engineering design, optimization, and real-time control across critical sectors.
The ability to adapt reduced-order models with constrained extreme gradient boosting changes how complex systems can be simulated and controlled, making high-fidelity analysis more accessible and dynamic.
- · Aerospace engineering
- · Automotive industry
- · Computational fluid dynamics researchers
- · AI/ML model developers
- · Entities reliant solely on expensive high-fidelity simulations
More efficient and faster design cycles for complex engineering systems become possible.
Reduced computational costs could lead to broader application of advanced simulations in smaller enterprises and novel research areas.
Accelerated innovation in areas like advanced manufacturing and defense due to enhanced simulation capabilities.
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