Deterministic and Stochastic Optimal Control and Inverse Problems

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advanced stochastic control theory
Aubin Property
Banach Space
Bayesian inference applications
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Equation Error Approach
feedback control systems
Fixed Point Iterations
functional analysis tools
Hemivariational Inequalities
Hilbert Scale
Ill Posed Operator Equations
Infinite Dimensional Banach Spaces
Lower Semicontinuous
Nonempty Convex Subset
numerical optimization methods
Optimal Control Problem
Paley Wiener Space
Parameter Choice Rule
Parameter Identification Problem
partial differential equations
Priori Parameter Choice
QR Decomposition
Reflexive Banach Space
regularization techniques
Riesz Basis
RKHS
Stochastic Collocation Method
Stochastic Galerkin
Stochastic Inverse Problems
Stochastic Optimal Control Problems
Stochastic PDEs
Stochastic Variational Inequality
Uncertainty Quantification
Variational Hemivariational Inequality
Variational Inequality
Weak Lower Semi Continuity

Product details

  • ISBN 9780367506308
  • Weight: 766g
  • Dimensions: 156 x 234mm
  • Publication Date: 15 Dec 2021
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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Inverse problems of identifying parameters and initial/boundary conditions in deterministic and stochastic partial differential equations constitute a vibrant and emerging research area that has found numerous applications. A related problem of paramount importance is the optimal control problem for stochastic differential equations.

This edited volume comprises invited contributions from world-renowned researchers in the subject of control and inverse problems. There are several contributions on optimal control and inverse problems covering different aspects of the theory, numerical methods, and applications. Besides a unified presentation of the most recent and relevant developments, this volume also presents some survey articles to make the material self-contained. To maintain the highest level of scientific quality, all manuscripts have been thoroughly reviewed.

Baasansuren Jadamba is an Associate Professor at the Rochester Institute of Technology. She received her Ph.D. from Friedrich-Alexander University Erlangen-Nuremberg in 2004. Her research interests are numerical analysis of partial differential equations, finite element methods, parameter identification problems in partial differential equations, and stochastic equilibrium problems.

Akhtar A. Khan is a Professor at the Rochester Institute of Technology. His research deals with set-valued Optimization, inverse problems, and variational inequalities. He is a co-author of Set-valued Optimization, Springer (2015), and Co-editor of Nonlinear Analysis and Variational Problems, Springer (2009). He is Co-Editor in Chief of the Journal of Applied and Numerical Optimization, and Editorial Board member of Optimization, Journal of Optimization Theory and Applications, and Journal of Nonlinear and Variational Analysis.

Stanisław Migórski received his Ph.D. and Habilitation from Jagiellonian University in Krakow (JUK). Currently, he is a Full and Chair Professor of Mathematics at the Faculty of Mathematics and Computer Science at JUK. He published research work in the field of mathematical analysis and applications (partial differential equations, variational inequalities, optimal control, and Optimization). He has edited and authored books from publishers Kluwer/Plenum, Springer and Chapman & Hall.

Miguel Sama is an associate professor at Universidad Nacional Educación a Distancia (Madrid, Spain). His research is broadly on Optimization, focusing mainly on Applied Mathematics Models. His research interests are in infinite-dimensional optimization problems. They cover a wide range of theoretical and applied topics such as Ordered Vector Spaces, Set-Valued Analysis, Vector, Set-Valued Optimization, PDE-constrained Optimization, Inverse Problems, Optimal Control Problems, and Uncertainty Quantification.