Adaptive sampling strategies for reduced-order modeling

Improved Reduced Order Modeling Strategies for Coupled and Parametric Systems

Parametric Reduced Order Models Using Adaptive Sampling and Interpolation

Over the past decade, a number of approaches have been put forth to improve the accuracy of projection-based reduced order models over parameter ranges. These can be classified as either i.) building a global basis that is suitable for a large parameter set by applying sampling strategies, ii.) identifying parameter dependent coefficient functions in the reduced order model, or iii.) changing t...

AN ADAPTIVE IMPORTANCE SAMPLING-BASED ALGORITHM USING THE FIRST-ORDER METHOD FOR STRUCTURAL RELIABILITY

Monte Carlo simulation (MCS) is a useful tool for computation of probability of failure in reliability analysis. However, the large number of samples, often required for acceptable accuracy, makes it time-consuming. Importance sampling is a method on the basis of MCS which has been proposed to reduce the computational time of MCS. In this paper, a new adaptive importance sampling-based algorith...

Adaptive $h$-refinement for reduced-order models

This work presents a method to adaptively refine reduced-order models a posteriori without requiring additional full-order-model solves. The technique is analogous to mesh-adaptive h-refinement: it enriches the reduced-basis space online by ‘splitting’ a given basis vector into several vectors with disjoint support. The splitting scheme is defined by a tree structure constructed offline via rec...

Reduced Order Adaptive Observer Backstepping

It is the intent of this paper to add to, and improve the existing design tools for nonlinear output feedback systems. Considered here are systems, exhibiting parametric uncertainty, and containing nonlinearities dependent on the output alone. A lower triangular state transformation is utilised, equivalent to that obtained via the traditional and recursive backstepping algorithm. Extra damping ...