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
标签： Optimization and Uncertainty Quantification
上传时间： 2016-04-08
上传用户：huhu123456
EECS150-Digital Design Lecture 1 - Introduction Electrical Engineering and Computer Sciences University of California, Berkeley
标签： lec01-intro 接口
上传时间： 2016-04-08
上传用户：sidewen666
color science; Provide a systematic introduction to the human vision system, color image formation, color reproduction, and color space.
标签： color science
上传时间： 2016-05-21
上传用户：wisewater
Fiddler is a free web debugging tool which logs all HTTP(S) traffic between your computer and the Internet. Inspect traffic, set breakpoints
上传时间： 2016-06-03
上传用户：ttry
cv 是computer vision，opencv是开发的计算机视觉处理算法。
标签： 计算机视觉的第一本书
上传时间： 2016-09-25
上传用户：fly_1797
Coding the Matrix _ Linear Algebra through Computer Science Applications 著名线性代数和矩阵课程的课本，亚马逊销量第一
上传时间： 2016-11-14
上传用户：kitool
MPICH2 Windows Development Guide∗ Version 1.0.6 Mathematics and Computer Science Division Argonne National Laboratory
标签： Development Windows Version MPICH2 Guide
上传时间： 2017-05-05
上传用户：BlackCuber
字符串,比较 从键盘输入一行字符，以‘$’结束，查找输入的字符串是否包含‘computer’字符串，如果包含，计算包含’computer’字符串的个数，并以十进制输出个数。
上传时间： 2017-12-05
上传用户：z993661909
从键盘输入一行字符，以‘$’结束，查找输入的字符串是否包含‘computer’字符串，如果包含，计算包含’computer’字符串的个数，并以十进制输出个数。
上传时间： 2017-12-05
上传用户：z993661909
ADSP Computer Exercise
标签： ADSP
上传时间： 2017-12-23
上传用户：詹姆斯邵
