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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
主要針對圖像處理的opencv開源庫應用
上傳時間： 2017-02-12
上傳用戶：xiaojiwei98
EEMD代碼學習，互相學習，共同進步。 EEMD代碼學習，互相學習，共同進步。
上傳時間： 2017-05-08
上傳用戶：1044109363@qq.com
CCS樣式選擇符，初學者，設計，DW，網頁制作，大一作業 部分預覽： <!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> <html xmlns="http://www.w3.org/1999/xhtml"> <head> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> <title>CSS樣式選擇符</title> <style type="text/css"> body { background-image:url(images/%E8%83%8C%E6%99%AF%E5%9B%BE%E7%89%87.jpg); background-repeat:repeat; } .class1 { text-align:center; font-weight:bolder; } .class2 { font-family:"仿宋"; text-indent:8em; } .class3 { font-size:18px; font-family:"宋體"; text-indent:4em; } #id1 { font-family:Zombie, Verdana, "Comic Sans MS"; font-style:oblique; font-size:64px; } #id2 { font-family:"黑體"; font-size:36px; } #id3 { color:#F69; font-weight:bolder; text-shadow:#FCC; } </style> </head> <body> <table width="780" height="1555" border="0" cellspacing="0" align="center" bgcolor="#FFFFFF"> <tr height="30"> <td align="center"><img src="images/頂部圖片.jpg" /></td> </tr>
上傳時間： 2017-12-07
上傳用戶：圈圈Ace
DEEP learning paper DEEP learning paper DEEP learning paper DEEP learning paper DEEP learning paper DEEP learning paper DEEP learning paper DEEP learning paper
上傳時間： 2018-06-13
上傳用戶：1203955829@qq.com
學習Vim的電子書。此書深淺適中，適合初學者循序漸進地入門，并逐步進入能力飛升的境界，可充分運用Vim制作腳本，搭建開發環境，以至可以完成相當種類的代碼生成和數據處理。
標簽： Learning Editors the and Vim vi
上傳時間： 2018-11-23
上傳用戶：milo
深度學習，神經網絡，卷積神經網絡 Analysis of Deep Learning Models using CNN Techniques
上傳時間： 2020-01-02
上傳用戶：wzy2020
Evolutionary Computation (EC) deals with problem solving, optimization, and machine learning techniques inspired by principles of natural evolution and ge- netics. Just from this basic definition, it is clear that one of the main features of the research community involved in the study of its theory and in its applications is multidisciplinarity. For this reason, EC has been able to draw the attention of an ever-increasing number of researchers and practitioners in several fields.
標簽： Applications Evolutionary Computing of
上傳時間： 2020-05-26
上傳用戶：shancjb
This introduction takes a visionary look at ideal cognitive radios (CRs) that inte- grate advanced software-defined radios (SDR) with CR techniques to arrive at radios that learn to help their user using computer vision, high-performance speech understanding, global positioning system (GPS) navigation, sophisticated adaptive networking, adaptive physical layer radio waveforms, and a wide range of machine learning processes.
標簽： Technology Cognitive Radio
上傳時間： 2020-05-26
上傳用戶：shancjb
Much of Game Theory has developed within the community of Economists, starting from the book “Theory of Games and Economic behavior” by Mor- genstern and Von Neumann (1944). To a lesser extent, it has had an impact on biology (with the development of evolutionarygames) and on road traffic Engi- neering (triggered by the concept of Wardrop equilibrium introduced already in 1952 along with the Beckmann potential approach introduced in 1956). Since 1999 game theory has had a remarkable penetration into computer sci- ence with the formation of the community of Algorithmic game theory.
標簽： Distributed Strategic Learning
上傳時間： 2020-05-27
上傳用戶：shancjb
