Design And Modeling for Computer Experiments

Designing engineering components that make optimal use of materials requires consideration of the nonlinear static and dynamic characteristics associated with both manufacturing and working environments. The modeling of these characteristics can only be done through numerical formulation and simulation, which requires an understanding of the theoretical background and associated computer solution techniques. In this two-volume set, the authors provide a unified treatment of both the static and dynamics aspects of nonlinear solid mechanics and associated finite element simulation techniques. Alongside numerous worked examples and exercises are user instructions, program descriptions and examples for MATLAB computer implementations with source code available online. While this book is designed to complement postgraduate courses, it is also relevant to industry professionals requiring an appreciation of the way their computer simulation programs work.

Many machine learning algorithms require real-valued feature vectors of data instances as inputs. By projecting data into vector spaces, representation learning techniques have achieved promising performance in many areas such as computer vision and natural language processing. There is also a need to learn representations for discrete relational data, namely networks or graphs. Network Embedding (NE) aims at learning vector representations for each node or vertex in a network to encode the topologic structure. Due to its convincing performance and efficiency, NE has been widely applied in many network applications such as node classification and link prediction.This book provides a comprehensive introduction to the basic concepts, models, and applications of network representation learning (NRL). The book starts with an introduction to the background and rising of network embeddings as a general overview for readers. Then it introduces the development of NE techniques by presenting se