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Author: Isaac Elishakoff
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 3111354733
Category : Technology & Engineering
Languages : en
Pages : 532
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Book Description
The book exposes three alternative and competing approaches to uncertainty analysis in engineering. It is composed of some essays on various sub-topics like random vibrations, probabilistic reliability, fuzzy-sets-based analysis, unknown-but-bounded variables, stochastic linearization, possible difficulties with stochastic analysis of structures.
Author: Isaac Elishakoff
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 3111354733
Category : Technology & Engineering
Languages : en
Pages : 532
Get Book Here
Book Description
The book exposes three alternative and competing approaches to uncertainty analysis in engineering. It is composed of some essays on various sub-topics like random vibrations, probabilistic reliability, fuzzy-sets-based analysis, unknown-but-bounded variables, stochastic linearization, possible difficulties with stochastic analysis of structures.
Author: Isaac Elishakoff
Publisher:
ISBN: 9783111354217
Category : Mathematics
Languages : en
Pages : 0
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Book Description
The book exposes three alternative and competing approaches to uncertainty analysis in engineering. It is composed of some essays on various sub-topics like random vibrations, probabilistic reliability, fuzzy-sets-based analysis, unknown-but-bounded variables, stochastic linearization, possible difficulties with stochastic analysis of structures.
Author: Yan Wang
Publisher: Woodhead Publishing
ISBN: 0081029411
Category : Technology & Engineering
Languages : en
Pages : 604
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Book Description
Uncertainty Quantification in Multiscale Materials Modeling provides a complete overview of uncertainty quantification (UQ) in computational materials science. It provides practical tools and methods along with examples of their application to problems in materials modeling. UQ methods are applied to various multiscale models ranging from the nanoscale to macroscale. This book presents a thorough synthesis of the state-of-the-art in UQ methods for materials modeling, including Bayesian inference, surrogate modeling, random fields, interval analysis, and sensitivity analysis, providing insight into the unique characteristics of models framed at each scale, as well as common issues in modeling across scales.
Author: Mohammed Elmusrati
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 3111585050
Category : Technology & Engineering
Languages : en
Pages : 332
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Book Description
This book delves into dynamic systems modeling, probability theory, stochastic processes, estimation theory, Kalman filters, and game theory. While many excellent books offer insights into these topics, our proposed book takes a distinctive approach, integrating these diverse subjects to address uncertainties and demonstrate their practical applications. The author aims to cater to a broad spectrum of readers. The book features approximately 150 meticulously explained solved examples and numerous simulation programs, each with detailed explanations. "Modelling Stochastic Uncertainties" provides a comprehensive understanding of uncertainties and their implications across various domains. Here is a brief exploration of the chapters: Chapter 1: Introduces the book's philosophy and the manifestation of uncertainties. Chapter 2: Lays the mathematical foundation, focusing on probability theory and stochastic processes, covering random variables, probability distributions, expectations, characteristic functions, and limits, along with various stochastic processes and their properties. Chapter 3: Discusses managing uncertainty through deterministic and stochastic dynamic modeling techniques. Chapter 4: Explores parameter estimation amid uncertainty, presenting key concepts of estimation theory. Chapter 5: Focuses on Kalman filters for state estimation amid uncertain measurements and Gaussian additive noise. Chapter 6: Examines how uncertainty influences decision-making in strategic interactions and conflict management. Overall, the book provides a thorough understanding of uncertainties, from theoretical foundations to practical applications in dynamic systems modeling, estimation, and game theory.
Author: Roland Platz
Publisher: Springer Nature
ISBN: 3031370031
Category : Technology & Engineering
Languages : en
Pages : 208
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Book Description
Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 41st IMAC, A Conference and Exposition on Structural Dynamics, 2023, the third volume of ten from the Conference brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Model Validation and Uncertainty Quantification, including papers on: Introduction of Uncertainty Quantification Uncertainty Quantification in Dynamics Model Form Uncertainty and Selection incl. Round Robin Challenge Sensor and Information Fusion Virtual Sensing, Certification, and Real-Time Monitoring Surrogate Modeling
Author: I. Almeida
Publisher: Now Next Later AI
ISBN: 0975642219
Category : Business & Economics
Languages : en
Pages : 302
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Book Description
In "Responsible AI in the Age of Generative Models: Governance, Ethics and Risk Management" we present a comprehensive guide to navigating the complex landscape of ethical AI development and deployment. As generative AI systems become increasingly powerful and ubiquitous, it is crucial to develop governance frameworks that mitigate potential risks while harnessing the technology's transformative potential. This book presents a rights-based approach, grounded in established human rights frameworks, to align AI systems with societal values and expectations. Divided into ten parts, the book covers a wide range of topics essential for responsible AI governance: Part I maps generative AI risks to specific human rights, while Part II presents a framework for institutionalizing rights-respecting AI practices throughout the development lifecycle. Part III delves into responsible data governance practices, and Part IV examines participatory approaches to data stewardship. Part V explores the roles and responsibilities of different organizational functions in operationalizing responsible AI, emphasizing the need for cross-functional collaboration. Transparency and algorithmic auditing are the focus of Part VI, followed by Part VII, which provides guidance on implementing effective multi-layered governance across the AI system lifecycle. Part VIII introduces maturity models for assessing an organization's responsible AI capabilities, and Part IX features an in-depth case study of Anthropic's innovative Constitutional AI approach. Finally, Part X analyzes emerging regulatory frameworks such as the EU AI Act and discusses the implications for businesses operating in multiple jurisdictions. "Responsible AI in the Age of Generative Models" equips readers with the knowledge, tools, and strategies needed to unlock the transformative potential of generative models while safeguarding human rights and promoting social justice. It is an essential resource for business leaders, policymakers, researchers, and anyone concerned about the future of AI governance. By embracing responsible AI as an imperative, we can work together to build a world where AI empowers and uplifts us all. This book is an invitation to engage in that critical conversation and take action towards a more equitable future.
Author: Jon Paul Janet
Publisher: American Chemical Society
ISBN: 0841299005
Category : Science
Languages : en
Pages : 197
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Book Description
Recent advances in machine learning or artificial intelligence for vision and natural language processing that have enabled the development of new technologies such as personal assistants or self-driving cars have brought machine learning and artificial intelligence to the forefront of popular culture. The accumulation of these algorithmic advances along with the increasing availability of large data sets and readily available high performance computing has played an important role in bringing machine learning applications to such a wide range of disciplines. Given the emphasis in the chemical sciences on the relationship between structure and function, whether in biochemistry or in materials chemistry, adoption of machine learning by chemistsderivations where they are important
Author: Y. Zee Ma
Publisher: AAPG
ISBN: 0891813780
Category : Science
Languages : en
Pages : 329
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Book Description
Author: Esmaeil Zarei
Publisher: Springer Nature
ISBN: 303162470X
Category : Electronic books
Languages : en
Pages : 537
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Book Description
Author: Daniel Williams
Publisher: Cambridge University Press
ISBN: 1009436112
Category : Literary Criticism
Languages : en
Pages : 345
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Book Description
Daniel Williams shows how, in a profoundly numerical age, Victorian novels imagined thought and action in the face of uncertainty.
