Real-Time PDE-Constrained Optimization

Partial differential equations, the workhorses of the engineering and scientific worlds, are singular and significant elements of design, control and parameter estimation. However, their potential complexity can make it impossible to achieve rapid solutions, particularly in the case of time-sensitive applications such as simulation-based decision making. Contributed by practitioners and academics, this focuses on new formulations, methods and algorithms researchers and engineers need to optimize PDE-controlled situations. Topics include the constrained optimal feedback control of systems governed by large differential algebraic equations , a stabilizing real-time implementation of nonlinear model prediction control, numerical feedback controller design, a least-squares finite element method, a collection of fast PDE-constrained optimization solvers, recommendations for reduced-order modeling and a range of applications. Annotation c2007 Book News, Inc., Portland, OR (booknews.com)

Lorenz T. Biegler is the Bayer Professor of Chemical Engineering at Carnegie Mellon University. His research interests are in the development and application of concepts in optimization theory, operations research, and numerical methods for process design, analysis, and control. Omar Ghattas is the John A. and Katherine G. Jackson Chair in Computational Geosciences at the University of Texas at Austin. His research focuses on optimization, parameter estimation, and uncertainty quantification for large-scale problems in the geological, mechanical, and biomedical engineering sciences. Matthias Heinkenschloss is Professor of Computational and Applied Mathematics at Rice University. His research interests are in numerical solution of large-scale optimization, optimal control, and parameter identification problems; domain decomposition methods; preconditioning of KKT systems; error estimation for optimal control problems; and applications in science and engineering. David E. Keyes is the Fu Foundation Professor of Applied Mathematics at Columbia University. He works at the algorithmic interface between parallel computing and the numerical analysis of partial differential equations, across a spectrum of aerodynamic, geophysical, and chemically reacting flows. Bart van Bloemen Waanders is Principal Member of the Technical Staff at Sandia National Laboratories. His main area of research is the development, analysis, and application of methods for PDE-constrained optimization, especially for large state and design spaces, including inverse problems, shape optimization, and design problems.