Computational models are commonly used in engineering design and scientific discovery activities for simulating
complex physical systems in disciplines such as fluid mechanics, structural dynamics, heat transfer, nonlinear
structural mechanics, shock physics, and many others. These simulators can be an enormous aid to engineers who
want to develop an understanding and/or predictive capability for complex behaviors typically observed in the
corresponding physical systems. Simulators often serve as virtual prototypes, where a set of predefined system
parameters, such as size or location dimensions and material properties, are adjusted to improve the performance
of a system, as defined by one or more system performance objectives. Such optimization or tuning of the
virtual prototype requires executing the simulator, evaluating performance objective(s), and adjusting the system
parameters in an iterative, automated, and directed way. System performance objectives can be formulated, for
example, to minimize weight, cost, or defects; to limit a critical temperature, stress, or vibration response; or
to maximize performance, reliability, throughput, agility, or design robustness. In addition, one would often
like to design computer experiments, run parameter studies, or perform uncertainty quantification (UQ). These
approaches reveal how system performance changes as a design or uncertain input variable changes. Sampling
methods are often used in uncertainty quantification to calculate a distribution on system performance measures,
and to understand which uncertain inputs contribute most to the variance of the outputs.
A primary goal for Dakota development is to provide engineers and other disciplinary scientists with a systematic
and rapid means to obtain improved or optimal designs or understand sensitivity or uncertainty using simulationbased
models. These capabilities generally lead to improved designs and system performance in earlier design
stages, alleviating dependence on physical prototypes and testing, shortening design cycles, and reducing product
development costs. In addition to providing this practical environment for answering system performance questions,
the Dakota toolkit provides an extensible platform for the research and rapid prototyping of customized
methods and meta-algorithms
標簽：
Optimization and Uncertainty Quantification
上傳時間：
2016-04-08
上傳用戶：huhu123456
The idea for this book was born during one of my project-related trips to the beautiful city
of Hangzhou in China, where in the role of Chief Architect I had to guide a team of very
young, very smart and extremely dedicated software developers and verification engineers.
Soon it became clear that as eager as the team was to jump into the coding, it did not have
any experience in system architecture and design and if I did not want to spend all my time in
constant travel between San Francisco and Hangzhou, the only option was to groom a number
of local junior architects. Logically, one of the first questions being asked by these carefully
selected future architects was whether I could recommend a book or other learning material
that could speed up the learning cycle. I could not. Of course, there were many books on
various related topics, but many of them were too old and most of the updated information
was either somewhere on the Internet dispersed between many sites and online magazines, or
buried in my brain along with many years of experience of system architecture.
標簽：
Telecommunication
Gateways
System
Design
for
上傳時間：
2020-06-01
上傳用戶：shancjb
