model reduction

Efficient non-linear model reduction via a least-squares Petrov–Galerkin projection and compressive tensor approximations

A Petrov--Galerkin projection method is proposed for reducing the dimension of a discrete non‐linear static or dynamic computational model in view of enabling its processing in real time. The right reduced‐order basis is chosen to be invariant and is …

A method for interpolating on manifolds structural dynamics reduced-order models

A rigorous method for interpolating a set of parameterized linear structural dynamics reduced‐order models (ROMs) is presented. By design, this method does not operate on the underlying set of parameterized full‐order models. Hence, it is amenable to …

An adaptive POD-Krylov reduced-order model for structural optimization

We present an adaptive proper orthogonal decomposition (POD)-Krylov reduced-order model (ROM) for structural optimization. At each step of the optimization loop, we compute approximate solutions to the structural state and sensitivity equations using …

A low-cost, goal-oriented ‘compact proper orthogonal decomposition’ basis for model reduction of static systems

Reduced basis methods are powerful tools that can significantly speed up computationally expensive analyses in a variety of 'many-query' and real-time applications, including de- sign optimization. Unfortunately, these techniques produce …