This book investigates in detail the deep learning (DL) techniques in electromagnetic (EM) near-field scattering problems, assessing its potential to replace traditional numerical solvers in real-time forecast scenarios. Studies on EM scattering problems have attracted researchers in various fields, such as antenna design, geophysical exploration and remote sensing. Pursuing a holistic perspective, the book introduces the whole workflow in utilizing the DL framework to solve the scattering problems. To achieve precise approximation, medium-scale data sets are sufficient in training the proposed model. As a result, the fully trained framework can realize three orders of magnitude faster than the conventional FDFD solver. It is worth noting that the 2D and 3D scatterers in the scheme can be either lossless medium or metal, allowing the model to be more applicable. This book is intended for graduate students who are interested in deep learning with computational electromagnetics, professional practitioners working on EM scattering, or other corresponding researchers.
This book presents a new front of research in conformal geometry, on sign-changing Yamabe-type problems and contact form geometry in particular. New ground is broken with the establishment of a Morse lemma at infinity for sign-changing Yamabe-type problems. This family of problems, thought to be out of reach a few years ago, becomes a family of problems which can be studied: the book lays the foundation for a program of research in this direction.In contact form geometry, a cousin of symplectic geometry, the authors prove a fundamental result of compactness in a variational problem on Legrendrian curves, which allows one to define a homology associated to a contact structure and a vector field of its kernel on a three-dimensional manifold. The homology is invariant under deformation of the contact form, and can be read on a sub-Morse complex of the Morse complex of the variational problem built with the periodic orbits of the Reeb vector-field. This book introduces, therefore, a practical tool in the field, and this homology becomes computable./a
This book creates the concept of “enterprise organization engineering” by introducing the paradigm of tissue engineering in life science into enterprise organization research. It regards the enterprise as live organization, which has life characters and ability to grow and self-repair. The authors seek origins from seven theories including human tissue engineering, evolutionary economics, organization theories, enterprise theories, entrepreneur theory, human recourse theory, knowledge management theory, and summarizes the research framework including five parts : research on enterprise life characteristics, enterprise genes, enterprise seed cells, enterprise life scaffolds and research on enterprise growth factors. This research framework, which bases on five principles, presents a new perspective for corporate management staff and riches management theories.
This book, adopting the perspective of cross-cultural communication, theoretically justifies and addresses human variational translation practice for the first time in the area of translation studies, focusing on the adaptation techniques and variational translation methods, as well as general features and laws of the variational translation process. It classifies and summarizes seven main adaptation techniques and eleven translation methods applicable to all variational translation activities. These techniques and methods, quite different from those used in complete translation or full translation, are systematically studied together with examples, allowing readers to not only understand their interrelations and differences within the context of variational translation methods, but also to master them in order to improve their translation efficacy and efficiency. Readers will gain a better understanding of how variational translation is produced, and of its important role in advancing cross-cultural communication and in reconstructing human knowledge and culture. This book is intended for translation scholars, translation practitioners, students, and others whose work involves the theory and practice of translation and who want to enhance their translation proficiency in cross-cultural communication for the Information Age.
This book investigates in detail the deep learning (DL) techniques in electromagnetic (EM) near-field scattering problems, assessing its potential to replace traditional numerical solvers in real-time forecast scenarios. Studies on EM scattering problems have attracted researchers in various fields, such as antenna design, geophysical exploration and remote sensing. Pursuing a holistic perspective, the book introduces the whole workflow in utilizing the DL framework to solve the scattering problems. To achieve precise approximation, medium-scale data sets are sufficient in training the proposed model. As a result, the fully trained framework can realize three orders of magnitude faster than the conventional FDFD solver. It is worth noting that the 2D and 3D scatterers in the scheme can be either lossless medium or metal, allowing the model to be more applicable. This book is intended for graduate students who are interested in deep learning with computational electromagnetics, professional practitioners working on EM scattering, or other corresponding researchers.
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