804 research outputs found
A non-overlapping optimization-based domain decomposition approach to component-based model reduction of incompressible flows
We present a component-based model order reduction procedure to efficiently
and accurately solve parameterized incompressible flows governed by the
Navier-Stokes equations. Our approach leverages a non-overlapping
optimization-based domain decomposition technique to determine the control
variable that minimizes jumps across the interfaces between sub-domains. To
solve the resulting constrained optimization problem, we propose both
Gauss-Newton and sequential quadratic programming methods, which effectively
transform the constrained problem into an unconstrained formulation.
Furthermore, we integrate model order reduction techniques into the
optimization framework, to speed up computations. In particular, we incorporate
localized training and adaptive enrichment to reduce the burden associated with
the training of the local reduced-order models. Numerical results are presented
to demonstrate the validity and effectiveness of the overall methodology
Differential-Algebraic Equations and Beyond: From Smooth to Nonsmooth Constrained Dynamical Systems
The present article presents a summarizing view at differential-algebraic
equations (DAEs) and analyzes how new application fields and corresponding
mathematical models lead to innovations both in theory and in numerical
analysis for this problem class. Recent numerical methods for nonsmooth
dynamical systems subject to unilateral contact and friction illustrate the
topicality of this development.Comment: Preprint of Book Chapte
A Preconditioned Inexact Active-Set Method for Large-Scale Nonlinear Optimal Control Problems
We provide a global convergence proof of the recently proposed sequential
homotopy method with an inexact Krylov--semismooth-Newton method employed as a
local solver. The resulting method constitutes an active-set method in function
space. After discretization, it allows for efficient application of
Krylov-subspace methods. For a certain class of optimal control problems with
PDE constraints, in which the control enters the Lagrangian only linearly, we
propose and analyze an efficient, parallelizable, symmetric positive definite
preconditioner based on a double Schur complement approach. We conclude with
numerical results for a badly conditioned and highly nonlinear benchmark
optimization problem with elliptic partial differential equations and control
bounds. The resulting method is faster than using direct linear algebra for the
2D benchmark and allows for the parallel solution of large 3D problems.Comment: 26 page
A velocity tracking approach for the Data Assimilation problem in blood flow simulations
preprintSeveral advances have been made in Data Assimilation techniques applied to blood flow modeling. Typically,idealized boundary conditions, only verified in straight parts of the vessel, are assumed. We present ageneral approach, based on a Dirichlet boundary control problem, that may potentially be used in differentparts of the arterial system. The relevance of this method appears when computational reconstructions ofthe 3D domains, prone to be considered sufficiently extended, are either not possible, or desirable, due tocomputational costs. Based on taking a fully unknown velocity profile as the control, the approach uses adiscretize then optimize methodology to solve the control problem numerically. The methodology is appliedto a realistic 3D geometry representing a brain aneurysm. The results show that this DA approach may bepreferable to a pressure control strategy, and that it can significantly improve the accuracy associated totypical solutions obtained using idealized velocity profilesinfo:eu-repo/semantics/submittedVersio
On Reduced Input-Output Dynamic Mode Decomposition
The identification of reduced-order models from high-dimensional data is a
challenging task, and even more so if the identified system should not only be
suitable for a certain data set, but generally approximate the input-output
behavior of the data source. In this work, we consider the input-output dynamic
mode decomposition method for system identification. We compare excitation
approaches for the data-driven identification process and describe an
optimization-based stabilization strategy for the identified systems
Convergence of Successive Linear Programming Algorithms for Noisy Functions
Gradient-based methods have been highly successful for solving a variety of
both unconstrained and constrained nonlinear optimization problems. In
real-world applications, such as optimal control or machine learning, the
necessary function and derivative information may be corrupted by noise,
however. Sun and Nocedal have recently proposed a remedy for smooth
unconstrained problems by means of a stabilization of the acceptance criterion
for computed iterates, which leads to convergence of the iterates of a
trust-region method to a region of criticality, Sun and Nocedal (2022).
We extend their analysis to the successive linear programming algorithm, Byrd
et al. (2023a,2023b), for unconstrained optimization problems with objectives
that can be characterized as the composition of a polyhedral function with a
smooth function, where the latter and its gradient may be corrupted by noise.
This gives the flexibility to cover, for example, (sub)problems arising image
reconstruction or constrained optimization algorithms.
We provide computational examples that illustrate the findings and point to
possible strategies for practical determination of the stabilization parameter
that balances the size of the critical region with a relaxation of the
acceptance criterion (or descent property) of the algorithm
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