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Engineering Science and Technology:
Innovations for the Future

Engineering Science and Technology: Innovations for the Future

Volume 2: Fungicides

Modern Crop Protection Compounds

Sustainable Computing and Optimization

Trends and Future Challenges

Managing Sustainable Operations of Logistics and Supply Chain Management

Selected Works Of Roderick S. C. Wong, The - Volume 4

Properties and Design

Multi-field Modelling of Soft Active Materials

A guide to the retro-tech we loved and lost

Gizmo

Triangulating AI Security

Practical Bioinformatics

Volume 3: Insecticides, 2 Volume Set

An Introduction to the Mechanics of Elastic and Plastic Deformation of Solids and Structural Materials

Mechanics of Materials

Expert reference on building surrogate models, optimization using them, prediction uncertainty associate with them, and their potential failure, with practical implementation in MATLAB Surrogate Modeling and Optimization explains the meaning of different surrogate models and provides an in-depth understanding of such surrogates, emphasizing how much uncertainty is associated with them, and when and how a surrogate model can fail in approximating complex functions and helping readers understand theory through practical implementation in MATLAB. This book enables readers to obtain an accurate approximate function using as few samples as possible, thereby allowing them to replace expensive computer simulations and experiments during design optimization, sensitivity analysis, and/or uncertainty quantification. The book is organized into three parts. Part I introduces the basics of surrogate modeling. Part II reviews various theories and algorithms of design optimization. Part III presents advanced topics in surrogate modeling, including the Kriging surrogate, neural network models, multi-fidelity surrogates, and efficient global optimization using Kriging surrogates. The book is divided into 10 chapters. Each chapter contains about 10 examples and 20 exercise problems. Lecture slides and a solution manual for exercise problems are available for instructors on a companion website. Sample topics discussed in Surrogate Modeling and Optimization include: <ul> <li>Various designs of experiments, such as those developed for linear and quadratic polynomial response surfaces (PRS) in a boxlike design space</li> <li>Criteria for constrained and unconstrained optimization and the most important optimization theories</li> <li>Various numerical algorithms for gradient-based optimization</li> <li>Gradient-free optimization algorithms, often referred to as global search algorithms, which do not require gradient or Hessian information</li> <li>Detailed explanations and implementation on Kriging surrogate, often referred to as Gaussian Process, especially when samples include noise</li> <li>The combination of a small number of high-fidelity samples with many low-fidelity samples to improve prediction accuracy</li> <li>Neural network models, focusing on training uncertainty and its effect on prediction uncertainty</li> <li>Efficient global optimization using either polynomial response surfaces or Kriging surrofates</li> </ul> Surrogate Modeling and Optimization is an essential learning companion for senior-level undergraduate and graduate students in all engineering disciplines, including mechanical, aerospace, civil, biomedical, and electrical engineering. The book is also valuable for industrial practitioners who apply the surrogate model to solve their optimization problems.

Theories, Applications, and Limitations

Surrogate Modeling and Optimization

Mathematics and Biomedical Engineering in Medicine, Physiology and Health Sciences is a visionary book, whose purpose is to demonstrate how fundamental mathematics and biomedical engineering can be incorporated into physiology, medicine, and health sciences teaching, research, and clinical practice to make these disciplines more quantitative and computational, and hence more explanatory and informative. The book also provides more quantitative formulation of medical procedures, towards supporting the growing field of precision medicine.

Mathematics and Biomedical Engineering in Medicine, Physiology and Health Sciences

Quantum Communication and Cryptography introduces readers to the theory of quantum cryptography, with a focus will on quantum key distribution (QKD) and more advanced quantum cryptographic protocols beyond QKD. It contains a brief introduction to the field of modern cryptography that is needed to fully appreciate and understand how quantum cryptographic systems are proven secure, and how they can be safely used in combination with current day classical systems. Readers are then introduced to quantum key distribution (QKD) - perhaps the most celebrated, and currently the most practical, of quantum cryptographic techniques. Basic protocols are described, and security proofs are given, providing readers with the knowledge needed to understand how QKD protocols are proven secure using modern, state- of-the-art definitions of security. Following this, more advanced QKD protocols are discussed, along with alternative quantum and classical methods to improve QKD performance. Finally, alternative quantum cryptographic protocols are covered, along with a discussion on some of the practical considerations of quantum secure communication technology. Throughout, protocols are described in a clear and consistent manner that still provides comprehensive, theoretical proofs and methods.

Quantum Communication and Cryptography

Engineering Science and Technology: Innovations for the Future

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