This book addresses computationally-efficient multi-objective optimization of antenna structures using variable-fidelity electromagnetic simulations, surrogate modeling techniques, and design space reduction methods.
With the primary goal of expanding access to spatial data science tools, this book offers dozens of minimal or low-code functions and tutorials designed to ease the implementation of fully reproducible Spatial Socio-Econometric Modeling (SSEM) analyses.
This Second Volume in the series Handbook of Dynamic Data Driven Applications Systems (DDDAS) expands the scope of the methods and the application areas presented in the first Volume and aims to provide additional and extended content of the increasing set of science and engineering advances for new capabilities enabled through DDDAS.
Numerical partial differential equations (PDEs) are an important part of numerical simulation, the third component of the modern methodology for science and engineering, besides the traditional theory and experiment.
This book describes the mathematical background behind discrete approaches to morphological analysis of scalar fields, with a focus on Morse theory and on the discrete theories due to Banchoff and Forman.
The Handbook of Simulation Optimization presents an overview of the state of the art of simulation optimization, providing a survey of the most well-established approaches for optimizing stochastic simulation models and a sampling of recent research advances in theory and methodology.
This volume explores how context has been and can be used in computing to model human behaviors, actions and communications as well as to manage data and knowledge.
This book portrays the commonality of tissue micro-structure that dictates physiological function in various organs (microstructure-function relation).
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduce the evolving area of static and dynamic simulation-based optimization.
Cutting-Edge Techniques to Better Analyze and Predict Complex Physical PhenomenaGeometric Modeling and Mesh Generation from Scanned Images shows how to integrate image processing, geometric modeling, and mesh generation with the finite element method (FEM) to solve problems in computational biology, medicine, materials science, and engineering.
This book is an attempt to present a unified theory of rare event simulation and the variance reduction technique known as importance sampling from the point of view of the probabilistic theory of large deviations.
In the past 15 to 20 years, the computer has become a popular tool for exploring the relationship between a measured response and factors thought to affect the response.
Discrete-event simulation consists of a collection of techniques that when applied to a discrete-event dynamical system, generates sequences called sample paths that characterize its behavior.
Physically based modeling is increasingly gaining acceptance within the computer graphics and mechanical engineering industries as a way of achiev- ing realistic animations and accurate simulations of complex systems.
