Quantification of Uncertainty: Improving Efficiency and Technology

Quantification of Uncertainty: Improving Efficiency and Technology
Author: Marta D'Elia
Publisher: Springer Nature
Total Pages: 290
Release: 2020-07-30
Genre: Mathematics
ISBN: 3030487210

This book explores four guiding themes – reduced order modelling, high dimensional problems, efficient algorithms, and applications – by reviewing recent algorithmic and mathematical advances and the development of new research directions for uncertainty quantification in the context of partial differential equations with random inputs. Highlighting the most promising approaches for (near-) future improvements in the way uncertainty quantification problems in the partial differential equation setting are solved, and gathering contributions by leading international experts, the book’s content will impact the scientific, engineering, financial, economic, environmental, social, and commercial sectors.

Handbook of Uncertainty Quantification

Handbook of Uncertainty Quantification
Author: Roger Ghanem
Publisher: Springer
Total Pages: 0
Release: 2016-05-08
Genre: Mathematics
ISBN: 9783319123844

The topic of Uncertainty Quantification (UQ) has witnessed massive developments in response to the promise of achieving risk mitigation through scientific prediction. It has led to the integration of ideas from mathematics, statistics and engineering being used to lend credence to predictive assessments of risk but also to design actions (by engineers, scientists and investors) that are consistent with risk aversion. The objective of this Handbook is to facilitate the dissemination of the forefront of UQ ideas to their audiences. We recognize that these audiences are varied, with interests ranging from theory to application, and from research to development and even execution.

Uncertainty Quantification in Laminated Composites

Uncertainty Quantification in Laminated Composites
Author: Sudip Dey
Publisher: CRC Press
Total Pages: 375
Release: 2018-09-19
Genre: Mathematics
ISBN: 1498784461

Over the last few decades, uncertainty quantification in composite materials and structures has gained a lot of attention from the research community as a result of industrial requirements. This book presents computationally efficient uncertainty quantification schemes following meta-model-based approaches for stochasticity in material and geometric parameters of laminated composite structures. Several metamodels have been studied and comparative results have been presented for different static and dynamic responses. Results for sensitivity analyses are provided for a comprehensive coverage of the relative importance of different material and geometric parameters in the global structural responses.

Bayesian Reinforcement Learning

Bayesian Reinforcement Learning
Author: Mohammad Ghavamzadeh
Publisher:
Total Pages: 146
Release: 2015-11-18
Genre: Computers
ISBN: 9781680830880

Bayesian methods for machine learning have been widely investigated, yielding principled methods for incorporating prior information into inference algorithms. This monograph provides the reader with an in-depth review of the role of Bayesian methods for the reinforcement learning (RL) paradigm. The major incentives for incorporating Bayesian reasoning in RL are that it provides an elegant approach to action-selection (exploration/exploitation) as a function of the uncertainty in learning, and it provides a machinery to incorporate prior knowledge into the algorithms. Bayesian Reinforcement Learning: A Survey first discusses models and methods for Bayesian inference in the simple single-step Bandit model. It then reviews the extensive recent literature on Bayesian methods for model-based RL, where prior information can be expressed on the parameters of the Markov model. It also presents Bayesian methods for model-free RL, where priors are expressed over the value function or policy class. Bayesian Reinforcement Learning: A Survey is a comprehensive reference for students and researchers with an interest in Bayesian RL algorithms and their theoretical and empirical properties.

Monte Carlo Strategies in Scientific Computing

Monte Carlo Strategies in Scientific Computing
Author: Jun S. Liu
Publisher: Springer Science & Business Media
Total Pages: 350
Release: 2013-11-11
Genre: Mathematics
ISBN: 0387763716

This book provides a self-contained and up-to-date treatment of the Monte Carlo method and develops a common framework under which various Monte Carlo techniques can be "standardized" and compared. Given the interdisciplinary nature of the topics and a moderate prerequisite for the reader, this book should be of interest to a broad audience of quantitative researchers such as computational biologists, computer scientists, econometricians, engineers, probabilists, and statisticians. It can also be used as a textbook for a graduate-level course on Monte Carlo methods.

Deep Active Learning

Deep Active Learning
Author: Kayo Matsushita
Publisher: Springer
Total Pages: 228
Release: 2017-09-12
Genre: Education
ISBN: 9811056609

This is the first book to connect the concepts of active learning and deep learning, and to delineate theory and practice through collaboration between scholars in higher education from three countries (Japan, the United States, and Sweden) as well as different subject areas (education, psychology, learning science, teacher training, dentistry, and business).It is only since the beginning of the twenty-first century that active learning has become key to the shift from teaching to learning in Japanese higher education. However, “active learning” in Japan, as in many other countries, is just an umbrella term for teaching methods that promote students’ active participation, such as group work, discussions, presentations, and so on.What is needed for students is not just active learning but deep active learning. Deep learning focuses on content and quality of learning whereas active learning, especially in Japan, focuses on methods of learning. Deep active learning is placed at the intersection of active learning and deep learning, referring to learning that engages students with the world as an object of learning while interacting with others, and helps the students connect what they are learning with their previous knowledge and experiences as well as their future lives.What curricula, pedagogies, assessments and learning environments facilitate such deep active learning? This book attempts to respond to that question by linking theory with practice.

Sensitivity & Uncertainty Analysis, Volume 1

Sensitivity & Uncertainty Analysis, Volume 1
Author: Dan G. Cacuci
Publisher: CRC Press
Total Pages: 304
Release: 2003-05-28
Genre: Mathematics
ISBN: 0203498798

As computer-assisted modeling and analysis of physical processes have continued to grow and diversify, sensitivity and uncertainty analyses have become indispensable investigative scientific tools in their own right. While most techniques used for these analyses are well documented, there has yet to appear a systematic treatment of the method based