Gradient-based constrained optimization using a database of linear reduced-order models

  title={Gradient-based constrained optimization using a database of linear reduced-order models},
  author={Youngsoo Choi and Gabriele Boncoraglio and Spenser Anderson and David Amsallem and Charbel Farhat},
  journal={J. Comput. Phys.},

A globally convergent method to accelerate large-scale optimization using on-the-fly model hyperreduction: application to shape optimization

At each iteration of the proposed algorithm, a reduced basis and empirical quadrature weights are constructed precisely to ensure the global convergence criteria of the trust-region method are satisfied, ensuring global convergence to a local minimum of the original (unreduced) problem.

Constrained multi-fidelity surrogate framework using Bayesian optimization with non-intrusive reduced-order basis

This article addresses the problem of constrained derivative-free optimization in a multi-fidelity (or variable-complexity) framework using Bayesian optimization techniques and proposes to use Gaussian process models with trend functions built from the projection of low-f fidelity solutions on a reduced-order basis synthesized from scarce high- fidelity snapshots.

Metamodeling techniques for CPU-intensive simulation-based design optimization: a survey

This paper reviews the strategies that seek to improve surrogate-based optimization efficiency, including ROM, multi-fidelity metamodeling, and DOE enrichment strategies.

Model Reduction Framework with a New Take on Active Subspaces for Optimization Problems with Linearized Fluid‐Structure Interaction Constraints

The obtained results illustrate the feasibility of the computational framework for realistic MDAO problems and highlight the benefits of the new approach for constructing an active subspace in both terms of solution optimality and wall-clock time reduction.

S-OPT: A Points Selection Algorithm for Hyper-Reduction in Reduced Order Models

It is found that using the S-OPT algorithm is shown to predict the full order solutions with higher accuracy for a given number of indices.

Model order reduction in aerodynamics: Review and applications

  • G. MendonçaF. AfonsoF. Lau
  • Computer Science
    Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering
  • 2019
A bibliographical research covering reduction of nonlinear, dynamic, or steady models was conducted, establishing the prevalence of projection and least mean squares methods, which rely on solutions of the original high-fidelity model and their proper orthogonal decomposition to work.

Component-wise reduced order model lattice-type structure design

Uncertainty quantification for industrial numerical simulation using dictionaries of reduced order models

The main contribution of this work is the application of the complete ROM-net workflow to the quantification of the uncertainty of dual quantities on this blade (such as the accumulated plastic strain and the stress tensor), generated by the uncertaintyof the temperature loading field.



Progressive construction of a parametric reduced‐order model for PDE‐constrained optimization

An adaptive approach to using reduced‐order models (ROMs) as surrogates in partial differential equations (PDE)‐constrained optimization is introduced that breaks the traditional offline‐online

Construction of Parametrically-Robust CFD-Based Reduced-Order Models for PDE-Constrained Optimization

Application to a standard, nonlinear CFD shape optimization problem shows that the proposed method effectively solves a PDE-constrained optimization problem with few full CFD simulation queries.

Design optimization using hyper-reduced-order models

It is shown in this paper that an additional approximation of the objective function is required by the construction of a surrogate objective using radial basis functions, and the proposed method is illustrated with two applications: the shape optimization of a simplified nozzle inlet model and the design optimized of a chemical reaction.

Accelerating Optimization of Parametric Linear Systems by Model Order Reduction

This paper replaces the standard first-order condition by the relaxed first- order condition, which is more suitable when algebraic reduced models are used as surrogate models, and proposes two optimization algorithms that uses the error bound to define a trust region and penalizes the objective with theerror bound.

Real-time solution of computational problems using databases of parametric linear reduced-order models with arbitrary underlying meshes

A comprehensive approach for real-time computations using a database of parameterized linear reduced-order models (ROMs) and a consistency enforcement approach for models defined on arbitrary underlying meshes are introduced.

On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming

A comprehensive description of the primal-dual interior-point algorithm with a filter line-search method for nonlinear programming is provided, including the feasibility restoration phase for the filter method, second-order corrections, and inertia correction of the KKT matrix.

A multi-point reduced-order modeling approach of transient structural dynamics with application to robust design optimization

A reduced-order modeling (ROM) framework for approximating the transient response of linear elastic structures over a range of design and random parameters and demonstrates significant savings in computational cost over using full-order models.

An adaptive and efficient greedy procedure for the optimal training of parametric reduced‐order models

An adaptive and efficient approach for constructing reduced‐order models (ROMs) that are robust to changes in parameters is developed. The approach is based on a greedy sampling of the underlying

Airfoil design optimization using reduced order models based on proper orthogonal decomposition

This paper presents a method for inviscid airfoil analysis and design optimization that uses reduced order models to reduce the cost of computation. Strong emphasis is placed on obtaining reasonably