Melika Baghi

Melika Baghi, Xiao Liu, Kamran Paynabar

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 constrained ensemble learning framework, termed Constrained Extreme Gradient Boosting (cXGBoost), for predicting Proper Orthogonal Decomposition (POD) bases as functions of system parameters. The approach leverages a geometric representation of subspaces on the Grassmann manifold, which are mapped to a Euclidean space to enable efficient regression using gradient boosting trees. A norm constraint is imposed during training to ensure the validity of the inverse mapping and preserve the geometric structure of the predicted subspaces. The proposed method is evaluated on four numerical examples, including fluid dynamics and wave propagation problems, demonstrating its ability to accurately predict parameter-dependent bases while maintaining robustness across nonlinear regimes. These results highlight the potential of combining geometric learning with constrained ensemble methods for scalable and reliable reduced-order modeling of high-dimensional parametric systems.

Melika Baghi, Hadi Mosadegh

Medical tourists face a scheduling problem that differs from that of local patients. Treatment delays extend not just care delivery time, but also accommodation and travel costs. This study develops a hybrid agent-based and discrete-event simulation model for an international patient department in a Tehran hospital case study. The model represents registration, consultation, admission, bed allocation, and discharge through discrete-event simulation, while patient, physician, and ward behaviours are represented through agent-based logic. A 256-run two-level fractional factorial design over 16 controllable factors is used to evaluate bed capacity, specialist counts, online consultation shares, bed-scheduling rules, patient-priority policy, and clinic slot interval across six performance measures. The primary outcome is the average waiting time of medical tourists in the hospital queue. In the case study, the hybrid model reduces this measure from 13.666 days in a DES-only counterpart to 2.416 days. It also reveals dropout and emergency-escalation patterns that a purely procedural representation suppresses. The results indicate that bed capacity, patient-priority rules, channel design, and clinic slot interval are the most influential levers for managing tourist waiting time. The study contributes a case-grounded decision-support model for hospital managers balancing shared capacity and service differentiation in medical tourism operations.