Active Manifold and Model Order Reduction to Accelerate Multidisciplinary Analysis and Optimization

Constraint C7 pertains to geometric and box constraints, making any surrogate model suitable for the task. The challenge lies in integrating various technologies to work together, with a focus on training. Training a surrogate model is costly due to the curse of dimensionality, regardless of the approach taken. This curse refers to the dimensionality of the parameter domain, not the model itself. Regression-based approaches offer some advantage in execution, while Proper Orthogonal Decomposition (PR) models excel in accuracy with comparable training data. The primary concern addressed in this presentation is the curse of dimensionality during training. Reduced Order Models are proposed as a technique to mitigate this challenge.