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Titlebook: Variational Methods for Structural Optimization; Andrej Cherkaev Book 2000 Springer-Verlag New York, Inc 2000 Algebra.Structural Optimizat

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書目名稱Variational Methods for Structural Optimization
編輯Andrej Cherkaev
視頻videohttp://file.papertrans.cn/981/980591/980591.mp4
叢書名稱Applied Mathematical Sciences
圖書封面Titlebook: Variational Methods for Structural Optimization;  Andrej Cherkaev Book 2000 Springer-Verlag New York, Inc 2000 Algebra.Structural Optimizat
描述In recent decades, it has become possible to turn the design process into computer algorithms. By applying different computer oriented methods the topology and shape of structures can be optimized and thus designs systematically improved. These possibilities have stimulated an interest in the mathematical foundations of structural optimization. The challenge of this book is to bridge a gap between a rigorous mathematical approach to variational problems and the practical use of algorithms of structural optimization in engineering applications. The foundations of structural optimization are presented in a sufficiently simple form to make them available for practical use and to allow their critical appraisal for improving and adapting these results to specific models. Special attention is to pay to the description of optimal structures of composites; to deal with this problem, novel mathematical methods of nonconvex calculus of variation are developed. The exposition is accompanied by examples.
出版日期Book 2000
關(guān)鍵詞Algebra; Structural Optimization; algorithm; algorithms; calculus; optimization
版次1
doihttps://doi.org/10.1007/978-1-4612-1188-4
isbn_softcover978-1-4612-7038-6
isbn_ebook978-1-4612-1188-4Series ISSN 0066-5452 Series E-ISSN 2196-968X
issn_series 0066-5452
copyrightSpringer-Verlag New York, Inc 2000
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Andrej Cherkaevn. Communication resources like the Network-on-Chip (NoC) on such platforms can be shared by such applications. Performance can be improved if the NoC is able to adapt at runtime to the requirements of different applications. An important challenge here is guaranteeing Quality of Service (QoS) for c
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Andrej Cherkaevomputing domain where energy consumption is a key factor to be considered by every designer. However, efficient hardware/software co-design still requires experience and a big effort: finding an optimal solution and an acceptable trade-off between performance and energy may require several tests and
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another decade, the omics data production rate is expected to be approaching one zettabase per year, at very low cost. There is dire need to bridge the gap between the capabilities of Next Generation Sequencing (NGS) technology in churning out omics big data and our computational capabilities in om
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Andrej CherkaevCNs). GCNs are a type of Graph Neural Networks (GNNs) that combine sparse and dense data compute requirements that are challenging to meet in resource-constrained embedded hardware. The gFADES architecture is optimized to work with the pruned data representations typically present in graph neural ne
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