Optimization and Control for Partial Differential Equations : : Uncertainty quantification, open and closed-loop control, and shape optimization / / ed. by Roland Herzog, Matthias Heinkenschloss, Dante Kalise, Georg Stadler, Emmanuel Trélat.

This book highlights new developments in the wide and growing field of partial differential equations (PDE)-constrained optimization. Optimization problems where the dynamics evolve according to a system of PDEs arise in science, engineering, and economic applications and they can take the form of i...

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Bibliographic Details
Superior document:Title is part of eBook package: De Gruyter DG Plus DeG Package 2022 Part 1
MitwirkendeR:
HerausgeberIn:
Place / Publishing House:Berlin ;, Boston : : De Gruyter, , [2022]
©2022
Year of Publication:2022
Language:English
Series:Radon Series on Computational and Applied Mathematics , 29
Online Access:
Physical Description:1 online resource (VIII, 466 p.)
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Table of Contents:
  • Frontmatter
  • Preface
  • Contents
  • 1 Reduced basis model order reduction in optimal control of a nonsmooth semilinear elliptic PDE
  • 2 Pointwise moving control for the 1-D wave equation
  • 3 Limits of stabilizability for a semilinear model for gas pipeline flow
  • 4 Minimal cost-time strategies for mosquito population replacement
  • 5 The sterile insect technique used as a barrier control against reinfestation
  • 6 Variational discretization approach applied to an optimal control problem with bounded measure controls
  • 7 An optimal control problem for equations with p-structure and its finite element discretization
  • 8 Unstructured space-time finite element methods for optimal sparse control of parabolic equations
  • 9 An adaptive finite element approach for lifted branched transport problems
  • 10 High-order homogenization of the Poisson equation in a perforated periodic domain
  • 11 Least-squares approaches for the 2D Navier–Stokes system
  • 12 Numerical issues and turnpike phenomenon in optimal shape design
  • 13 Feedback stabilization of Cahn–Hilliard phase-field systems
  • 14 Ensemble Kalman filter for neural network-based one-shot inversion
  • 15 Deep learning in high dimension: ReLU neural network expression for Bayesian PDE inversion
  • Index