Systems Patterns And Data Engineering With Geometric Calculi
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Systems, Patterns and Data Engineering with Geometric Calculi
Author: Sebastià Xambó-Descamps
language: en
Publisher: Springer Nature
Release Date: 2021-07-16
The intention of this collection agrees with the purposes of the homonymous mini-symposium (MS) at ICIAM-2019, which were to overview the essentials of geometric calculus (GC) formalism, to report on state-of-the-art applications showcasing its advantages and to explore the bearing of GC in novel approaches to deep learning. The first three contributions, which correspond to lectures at the MS, offer perspectives on recent advances in the application GC in the areas of robotics, molecular geometry, and medical imaging. The next three, especially invited, hone the expressiveness of GC in orientation measurements under different metrics, the treatment of contact elements, and the investigation of efficient computational methodologies. The last two, which also correspond to lectures at the MS, deal with two aspects of deep learning: a presentation of a concrete quaternionic convolutional neural network layer for image classification that features contrast invariance and a general overview of automatic learning aimed at steering the development of neural networks whose units process elements of a suitable algebra, such as a geometric algebra. The book fits, broadly speaking, within the realm of mathematical engineering, and consequently, it is intended for a wide spectrum of research profiles. In particular, it should bring inspiration and guidance to those looking for materials and problems that bridge GC with applications of great current interest, including the auspicious field of GC-based deep neural networks.
Systems, Patterns and Data Engineering with Geometric Calculi
The intention of this collection agrees with the purposes of the homonymous mini-symposium (MS) at ICIAM-2019, which were to overview the essentials of geometric calculus (GC) formalism, to report on state-of-the-art applications showcasing its advantages and to explore the bearing of GC in novel approaches to deep learning. The first three contributions, which correspond to lectures at the MS, offer perspectives on recent advances in the application GC in the areas of robotics, molecular geometry, and medical imaging. The next three, especially invited, hone the expressiveness of GC in orientation measurements under different metrics, the treatment of contact elements, and the investigation of efficient computational methodologies. The last two, which also correspond to lectures at the MS, deal with two aspects of deep learning: a presentation of a concrete quaternionic convolutional neural network layer for image classification that features contrast invariance and a general overview of automatic learning aimed at steering the development of neural networks whose units process elements of a suitable algebra, such as a geometric algebra. The book fits, broadly speaking, within the realm of mathematical engineering, and consequently, it is intended for a wide spectrum of research profiles. In particular, it should bring inspiration and guidance to those looking for materials and problems that bridge GC with applications of great current interest, including the auspicious field of GC-based deep neural networks.
Multivariate Calculus and Geometry Concepts
"Multivariate Calculus and Geometry Concepts" is a comprehensive textbook designed to provide students, researchers, and practitioners with a thorough understanding of fundamental concepts, techniques, and applications in multivariate calculus and geometry. Authored by experts, we offer a balanced blend of theoretical foundations, practical examples, and computational methods, making it suitable for both classroom instruction and self-study. We cover a wide range of topics, including partial derivatives, gradients, line and surface integrals, parametric equations, polar coordinates, conic sections, and differential forms. Each topic is presented clearly and concisely, with detailed explanations and illustrative examples to aid understanding. Our emphasis is on developing a conceptual understanding of key concepts and techniques, rather than rote memorization of formulas. We include numerous figures, diagrams, and geometric interpretations to help readers visualize abstract mathematical concepts and their real-world applications. Practical applications of multivariate calculus and geometry are highlighted throughout the book, with examples drawn from physics, engineering, computer graphics, and other fields. We demonstrate how these concepts are used to solve real-world problems and inspire readers to apply their knowledge in diverse areas. We discuss computational methods and numerical techniques used in multivariate calculus and geometry, such as numerical integration, optimization algorithms, and finite element methods. Programming exercises and computer simulations provide hands-on experience with implementing and applying these methods. Our supplementary resources include online tutorials, solution manuals, and interactive simulations, offering additional guidance, practice problems, and opportunities for further exploration and self-assessment. "Multivariate Calculus and Geometry Concepts" is suitable for undergraduate and graduate students in mathematics, engineering, physics, computer science, and related disciplines. It also serves as a valuable reference for researchers, educators, and professionals seeking a comprehensive overview of multivariate calculus and geometry and its applications in modern science and technology.