David Keyes - Böcker

This book focuses on computational methods for large-scale statistical inverse problems and provides an introduction to statistical Bayesian and frequentist methodologies. Recent research advances for approximation methods are discussed, along with Kalman filtering methods and optimization-based approaches to solving inverse problems. The aim is to cross-fertilize the perspectives of researchers in the areas of data assimilation, statistics, large-scale optimization, applied and computational mathematics, high performance computing, and cutting-edge applications. The solution to large-scale inverse problems critically depends on methods to reduce computational cost. Recent research approaches tackle this challenge in a variety of different ways. Many of the computational frameworks highlighted in this book build upon state-of-the-art methods for simulation of the forward problem, such as, fast Partial Differential Equation (PDE) solvers, reduced-order models and emulators of the forward problem, stochastic spectral approximations, and ensemble-based approximations, as well as exploiting the machinery for large-scale deterministic optimization through adjoint and other sensitivity analysis methods.Key Features: Brings together the perspectives of researchers in areas of inverse problems and data assimilation.Assesses the current state-of-the-art and identify needs and opportunities for future research.Focuses on the computational methods used to analyze and simulate inverse problems.Written by leading experts of inverse problems and uncertainty quantification.Graduate students and researchers working in statistics, mathematics and engineering will benefit from this book.

Many engineering and scientific problems in design, control, and parameter estimation can be formulated as optimization problems that are governed by partial differential equations (PDEs). The complexities of the PDEs - and the requirement for rapid solution - pose significant difficulties. A particularly challenging class of PDE-constrained optimization problems is characterized by the need for real-time solution, i.e., in time scales that are sufficiently rapid to support simulation-based decision making.Real-Time PDE-Constrained Optimization, the first book devoted to real-time optimization for systems governed by PDEs, focuses on new formulations, methods, and algorithms needed to facilitate real-time, PDE-constrained optimization. In addition to presenting state-of-the-art algorithms and formulations, the text illustrates these algorithms with a diverse set of applications that includes problems in the areas of aerodynamics, biology, fluid dynamics, medicine, chemical processes, homeland security, and structural dynamics. Despite difficulties, there is a pressing need to capitalize on continuing advances in computing power to develop optimization methods that will replace simple rule-based decision making with optimized decisions based on complex PDE simulations.

For statisticians, R is the go-to programming language for complex numerical analysis - but it comes in handy for a lot more than that. In R Without Statistics you'll discover ways that R can be used by the rest of us! Packed with real-world examples and easy-to-follow coding instructions, it introduces R's application in a wide range of non-statistical tasks, from data visualization and interpreting survey results, to map plotting and automating workloads. Each chapter features an actual R programmer who achieved something novel using the language, and then covers the case study and code samples demonstrating exactly how they did it. Whether it's creating visualizations for Scientific American, applying a consistent theme to BBC graphics, organizing professional government reports, or effectively mapping the spread of COVID-19, R offers a unique way to transform your work.

This book constitutes the refereed proceedings of the 32nd International Conference, ISC High Performance 2017, held in Frankfurt, Germany, in June 2017. The 22 revised full papers presented in this book were carefully reviewed and selected from 66 submissions. The papers cover the following topics: applications and algorithms;

This book constitutes the refereed proceedings of the 33rd International Conference, ISC High Performance 2018, held in Frankfurt, Germany, in June 2018.The 20 revised full papers presented in this book were carefully reviewed and selected from 81 submissions. The papers cover the following topics: Resource Management and Energy Efficiency; Performance Analysis and Tools; Exascale Networks; Parallel Algorithms.