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.

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.

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.

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. - Synthesizes available UQ methods for materials modeling - Provides practical tools and examples for problem solving in modeling material behavior across various length scales - Demonstrates UQ in density functional theory, molecular dynamics, kinetic Monte Carlo, phase field, finite element method, multiscale modeling, and to support decision making in materials design - Covers quantum, atomistic, mesoscale, and engineering structure-level modeling and simulation

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

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ARTIFICIAL INTELLIGENCE IN PERFORMANCE-DRIVEN DESIGN A definitive, interdisciplinary reference to using artificial intelligence technology and data-driven methodologies for sustainable design Artificial Intelligence in Performance-Driven Design: Theories, Methods, and Tools explores the application of artificial intelligence (AI), specifically machine learning (ML), for performance modeling within the built environment. This work develops the theoretical foundations and methodological frameworks for utilizing AI/ML, with an emphasis on multi-scale modeling encompassing energy flows, environmental quality, and human systems. The book examines relevant practices, case studies, and computational tools that harness AI’s capabilities in modeling frameworks, enhancing the efficiency, accuracy, and integration of physics-based simulation, optimization, and automation processes. Furthermore, it highlights the integration of intelligent systems and digital twins throughout the lifecycle of the built environment, to enhance our understanding and management of these complex environments. This book also: Incorporates emerging technologies into practical ideas to improve performance analysis and sustainable design Presents data-driven methodologies and technologies that integrate into modeling and design platforms Shares valuable insights and tools for developing decarbonization pathways in urban buildings Includes contributions from expert researchers and educators across a range of related fields Artificial Intelligence in Performance-Driven Design is ideal for architects, engineers, planners, and researchers involved in sustainable design and the built environment. It’s also of interest to students of architecture, building science and technology, urban design and planning, environmental engineering, and computer science and engineering.