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
Over the past ten years there has been a revolution in the devel-
opment and acceptance of mobile products. In that period, cel-
lular telephony and consumer electronics have moved from the
realm of science fiction to everyday reality. Much of that revolu-
tion is unremarkable – we use wireless, in its broadest sense, for
TV remote controls, car keyfobs, travel tickets and credit card
transactions every day. At the same time, we have increased the
number of mobile devices that we carry around with us. However,
in many cases the design and function of these and other static
products are still constrained by the wired connections that they
use to transfer and share data.
標簽：
Short-Range
Essentials
Wireless
of
上傳時間：
2020-05-27
上傳用戶：shancjb
Convergence between the two largest networks (Telecom and IP) is taking place
very rapidly and at diff erent levels: (1) network level: unifi cation of IP networks
with traditional Telecom networks through evolving standards (Session Initiation
Protocol (SIP), Realtime transfer Protocol (RTP), SS7, 3G) to support interopera-
bility; (2) service level: traditional Telecom services like voice calls are being provi-
sioned on the IP backbone (VoIP), while traditional IP services (most data-driven
services such as multimedia, browsing, chatting, gaming, etc.) are accessible over
the Telecom network.
標簽：
Platforms
Delivery
Service
上傳時間：
2020-06-01
上傳用戶：shancjb
