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With contributions by specialists in optimization and practitioners
in the fields of aerospace engineering, chemical engineering, and
fluid and solid mechanics, the major themes include an assessment
of the state of the art in optimization algorithms as well as
challenging applications in design and control, in the areas of
process engineering and systems with partial differential equation
models.
With contributions by specialists in optimization and practitioners
in the fields of aerospace engineering, chemical engineering, and
fluid and solid mechanics, the major themes include an assessment
of the state of the art in optimization algorithms as well as
challenging applications in design and control, in the areas of
process engineering and systems with partial differential equation
models.
With contributions by specialists in optimization and practitioners
in the fields of aerospace engineering, chemical engineering, and
fluid and solid mechanics, the major themes include an assessment
of the state of the art in optimization algorithms as well as
challenging applications in design and control, in the areas of
process engineering and systems with partial differential equation
models.
Optimal design, optimal control, and parameter estimation of systems governed by partial differential equations (PDEs) give rise to a class of problems known as PDE-constrained optimization. The size and complexity of the discretized PDEs often pose significant challenges for contemporary optimization methods. With the maturing of technology for PDE simulation, interest has now increased in PDE-based optimization. The chapters in this volume collectively assess the state of the art in PDE-constrained optimization, identify challenges to optimization presented by modern highly parallel PDE simulation codes, and discuss promising algorithmic and software approaches for addressing them. These contributions represent current research of two strong scientific computing communities, in optimization and PDE simulation. This volume merges perspectives in these two different areas and identifies interesting open questions for further research.
With contributions by specialists in optimization and practitioners in the fields of aerospace engineering, chemical engineering, and fluid and solid mechanics, the major themes include an assessment of the state of the art in optimization algorithms as well as challenging applications in design and control, in the areas of process engineering and systems with partial differential equation models.
With contributions by specialists in optimization and practitioners in the fields of aerospace engineering, chemical engineering, and fluid and solid mechanics, the major themes include an assessment of the state of the art in optimization algorithms as well as challenging applications in design and control, in the areas of process engineering and systems with partial differential equation models.
Inverse problems and optimal design have come of age as a
consequence of the availability of better, more accurate, and more
efficient simulation packages. Many of these simulators, which can
run on small workstations, can capture the complicated behavior of
the physical systems they are modeling, and have become commonplace
tools in engineering and science. There is a great desire to use
them as part of a process by which measured field data are analyzed
or by which design of a product is automated. A major obstacle in
doing precisely this is that one is ultimately confronted with a
large-scale optimization problem. This volume contains expository
articles on both inverse problems and design problems formulated as
optimization. Each paper describes the physical problem in some
detail and is meant to be accessible to researchers in optimization
as well as those who work in applied areas where optimization is a
key tool. What emerges in the presentations is that there are
features about the problem that must be taken into account in
posing the objective function, and in choosing an optimization
strategy. In particular there are certain structures peculiar to
the problems that deserve special treatment, and there is ample
opportunity for parallel computation. THIS IS BACK COVER TEXT
Inverse problems and optimal design have come of age as a
consequence of the availability of better, more accurate, and more
efficient, simulation packages. The problem of determining the
parameters of a physical system from
This book addresses modern nonlinear programming (NLP) concepts and
algorithms, especially as they apply to challenging applications in
chemical process engineering. The author provides a firm grounding
in fundamental NLP properties and algorithms, and relates them to
real-world problem classes in process optimization, thus making the
material understandable and useful to chemical engineers and
experts in mathematical optimization. Nonlinear Programming shows
readers which NLP methods are best suited for specific
applications, how large-scale problems should be formulated and
what features of these problems should be emphasized, and how
existing NLP methods can be extended to exploit specific structures
of large-scale optimization models.
Many engineering and scientific problems in design, control, and
parameter estimation can be formulated as optimization problems
that are governed by partial differential equations (PDEs). The
complexities of the PDEs - and the requirement for rapid solution -
pose significant difficulties. A particularly challenging class of
PDE-constrained optimization problems is characterized by the need
for real-time solution, i.e., in time scales that are sufficiently
rapid to support simulation-based decision making. Real-Time
PDE-Constrained Optimization, the first book devoted to real-time
optimization for systems governed by PDEs, focuses on new
formulations, methods, and algorithms needed to facilitate
real-time, PDE-constrained optimization. In addition to presenting
state-of-the-art algorithms and formulations, the text illustrates
these algorithms with a diverse set of applications that includes
problems in the areas of aerodynamics, biology, fluid dynamics,
medicine, chemical processes, homeland security, and structural
dynamics. Despite difficulties, there is a pressing need to
capitalize on continuing advances in computing power to develop
optimization methods that will replace simple rule-based decision
making with optimized decisions based on complex PDE simulations.
Differential-algebraic equations (DAEs) are the most natural way to
mathematically model many complex systems in science and
engineering. This book provides a guide to the theory and practice
of modelling with DAEs. In particular, the reader will learn to
maximise the performance of their models by optimising the design
parameters. Presented within are cutting-edge theory and
state-of-the-art numerical methods for the optimal control of
differential-algebraic equations, alongside real-world applications
of the results. This accessible treatment of the subject, written
by leading experts, is suitable for applied mathematicians,
engineers and computational scientists from a variety of
disciplines. It will be of interest to those developing theory and
those working on real-world applications, especially in the optimal
control of problems in chemical and mechanical engineering.
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