Proceedings of the 5th International Symposium on Uncertainty Quantification and Stochastic Modelling: Uncertainties 2020

This proceedings book covers state-of-the-art research on uncertainty quantification in mechanical engineering, based on papers presented at Uncertainties 2020, a workshop organized by several societies.

Format: Paperback / softback
Length: 472 pages
Publication date: 20 August 2021
Publisher: Springer Nature Switzerland AG

This proceedings book delves into cutting-edge research on uncertainty quantification in mechanical engineering, encompassing a comprehensive analysis of statistical data related to the inputs and parameters of a system, leading to the generation of statistical data on the system's outputs. Drawing from papers presented at the esteemed workshop "Uncertainties 2020," organized in collaboration with the Scientific Committee on Uncertainty in Mechanics (Mécanique et Incertain) of the AFM (French Society of Mechanical Sciences), the Scientific Committee on Stochastic Modeling and Uncertainty Quantification of the ABCM (Brazilian Society of Mechanical Sciences), and the SBMAC (Brazilian Society of Applied Mathematics), this publication offers a comprehensive exploration of the field.

The first chapter provides an overview of the current state-of-the-art in uncertainty quantification, highlighting key developments and challenges in the field. It explores the use of statistical methods, such as Bayesian inference and Monte Carlo simulation, to estimate the uncertainties associated with mechanical systems. The chapter also discusses the importance of considering the physical and geometric complexity of the system when performing uncertainty quantification, as well as the role of experimental data in validating and improving the models.

The second chapter focuses on the application of uncertainty quantification in mechanical engineering. It showcases case studies from various industries, including aerospace, automotive, and energy, where uncertainty quantification has been used to optimize design, improve reliability, and reduce costs. The chapter discusses the use of simulation-based optimization techniques, such as genetic algorithms and finite element analysis, to identify the most critical parameters and optimize the system's performance.

The third chapter explores the development of new uncertainty quantification methods and tools. It discusses the use of machine learning algorithms, such as deep learning, to model complex systems and improve the accuracy of uncertainty estimates. The chapter also explores the use of data-driven optimization techniques, such as Bayesian optimization and particle swarm optimization, to find the optimal solution to complex optimization problems.

The fourth chapter examines the role of uncertainty quantification in decision-making processes in mechanical engineering. It discusses the use of risk analysis and decision-making tools, such as cost-benefit analysis and decision trees, to evaluate the risks and benefits of different design options and make informed decisions. The chapter also explores the use of uncertainty quantification in safety analysis and risk management, such as fault tolerance analysis and reliability engineering.

The fifth chapter explores the challenges and limitations of uncertainty quantification in mechanical engineering. It discusses the challenges associated with modeling complex systems, such as the presence of uncertainties in material properties and boundary conditions. The chapter also discusses the challenges associated with data collection and analysis, such as the need for high-quality experimental data and the complexity of data processing.

The final chapter provides an outlook on the future of uncertainty quantification in mechanical engineering. It discusses the potential for further research and development in the field, including the use of advanced simulation techniques, the integration of artificial intelligence and machine learning, and the development of new uncertainty quantification methods and tools. The chapter also highlights the importance of interdisciplinary collaboration and the need for industry-academic partnerships to advance the field.

In conclusion, this proceedings book provides a comprehensive and up-to-date overview of uncertainty quantification in mechanical engineering. It offers a valuable resource for researchers, practitioners, and students interested in the field, covering a wide range of topics and applications. By presenting the latest research and case studies, the book contributes to the advancement of uncertainty quantification in mechanical engineering and its potential to improve the design, optimization, and reliability of mechanical systems.
Uncertainty quantification in mechanical engineering is a crucial aspect of designing and analyzing complex systems. This proceedings book, based on papers presented at the workshop "Uncertainties 2020," provides a comprehensive exploration of the state-of-the-art research in this field.

The first chapter provides an overview of uncertainty quantification, highlighting the importance of considering uncertainties in mechanical systems and the various methods used to estimate and manage them. It discusses the use of statistical methods, such as Bayesian inference and Monte Carlo simulation, to model and analyze the behavior of mechanical systems. The chapter also emphasizes the role of experimental data in validating and improving uncertainty quantification models.

The second chapter focuses on the application of uncertainty quantification in mechanical engineering. It showcases case studies from various industries, including aerospace, automotive, and energy, where uncertainty quantification has been used to optimize design, improve reliability, and reduce costs. The chapter discusses the use of simulation-based optimization techniques, such as genetic algorithms and finite element analysis, to identify the most critical parameters and optimize the system's performance.

The third chapter explores the development of new uncertainty quantification methods and tools. It discusses the use of machine learning algorithms, such as deep learning, to model complex systems and improve the accuracy of uncertainty estimates. The chapter also explores the use of data-driven optimization techniques, such as Bayesian optimization and particle swarm optimization, to find the optimal solution to complex optimization problems.

The fourth chapter examines the role of uncertainty quantification in decision-making processes in mechanical engineering. It discusses the use of risk analysis and decision-making tools, such as cost-benefit analysis and decision trees, to evaluate the risks and benefits of different design options and make informed decisions. The chapter also explores the use of uncertainty quantification in safety analysis and risk management, such as fault tolerance analysis and reliability engineering.

The fifth chapter explores the challenges and limitations of uncertainty quantification in mechanical engineering. It discusses the challenges associated with modeling complex systems, such as the presence of uncertainties in material properties and boundary conditions. The chapter also discusses the challenges associated with data collection.

The final chapter provides an outlook on the future of uncertainty quantification in mechanical engineering. It discusses the potential for further research and development in the field, including the use of advanced simulation techniques, the integration of artificial intelligence and machine learning, and the development of new uncertainty quantification methods and tools. The chapter also highlights the importance of interdisciplinary collaboration and the need for industry-academic partnerships to advance the field.

In conclusion, this proceedings book provides a comprehensive and up-to-date overview of uncertainty quantification in mechanical engineering. It offers a valuable resource for researchers, practitioners, and students interested in the field, covering a wide range of topics and applications. By presenting the latest research and case studies, the book contributes to the advancement of uncertainty quantification in mechanical engineering and its potential to improve the design, optimization, and reliability of mechanical systems.

Weight: 735g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030536718
Edition number: 1st ed. 2021