AI RESEARCH

Constrained Extreme Gradient Boosting for Adapting Reduced-Order Models

arXiv CS.LG

ArXi:2605.04130v1 Announce Type: new 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.