From Observations to Simulations


Book Description

The dynamics of a global economy is being reshaped by the economic emergence of two Asian giants, China and India. How the world's two most populous countries manage globalization as they pursue economic reform and liberalization will impact significantly their societies, the rest of Asia, and the world. This book brings together articles by first rate scholars of China and India to share and discuss their research findings in four areas: Challenges, Opportunities and Responses to Globalization; Social Security and Governance; National Security in the age of Globalization; and Ethnicity and Identity in the New World. The book includes an opening address by Singapore's Minister Mentor Lee Kuan Yew, from his speech on "Managing Globalization: Lessons from China and India", delivered at the official opening of the Lee Kuan Yew School of Public Policy on 4 April 2005.




From Observations To Simulations: A Conceptual Introduction To Weather And Climate Modelling


Book Description

From Observations to Simulations leads us on a fascinating journey through the methods used for the scientific analysis of complex systems such as the atmosphere and the Earth system: from meteorology and climatology, as observational sciences, to the development of models and the use of computers as virtual laboratories. In plain, accessible language, avoiding technicalities, but highlighting the conceptually meaningful aspects, the book describes this “Copernican revolution” in meteorology and climatology, a change in methodological paradigm that rigorously tests the definition of some classical concepts, such as “causality” and “prediction.” This is the first book that guides the general public (and sets the specialists thinking) through research on complex systems which is contributing to a change in our outlook on nature.




Practical Simulations for Machine Learning


Book Description

Simulation and synthesis are core parts of the future of AI and machine learning. Consider: programmers, data scientists, and machine learning engineers can create the brain of a self-driving car without the car. Rather than use information from the real world, you can synthesize artificial data using simulations to train traditional machine learning models.That’s just the beginning. With this practical book, you’ll explore the possibilities of simulation- and synthesis-based machine learning and AI, concentrating on deep reinforcement learning and imitation learning techniques. AI and ML are increasingly data driven, and simulations are a powerful, engaging way to unlock their full potential. You'll learn how to: Design an approach for solving ML and AI problems using simulations with the Unity engine Use a game engine to synthesize images for use as training data Create simulation environments designed for training deep reinforcement learning and imitation learning models Use and apply efficient general-purpose algorithms for simulation-based ML, such as proximal policy optimization Train a variety of ML models using different approaches Enable ML tools to work with industry-standard game development tools, using PyTorch, and the Unity ML-Agents and Perception Toolkits




Simulation and Its Discontents


Book Description

How the simulation and visualization technologies so pervasive in science, engineering, and design have changed our way of seeing the world. Over the past twenty years, the technologies of simulation and visualization have changed our ways of looking at the world. In Simulation and Its Discontents, Sherry Turkle examines the now dominant medium of our working lives and finds that simulation has become its own sensibility. We hear it in Turkle's description of architecture students who no longer design with a pencil, of science and engineering students who admit that computer models seem more “real” than experiments in physical laboratories. Echoing architect Louis Kahn's famous question, “What does a brick want?”, Turkle asks, “What does simulation want?” Simulations want, even demand, immersion, and the benefits are clear. Architects create buildings unimaginable before virtual design; scientists determine the structure of molecules by manipulating them in virtual space; physicians practice anatomy on digitized humans. But immersed in simulation, we are vulnerable. There are losses as well as gains. Older scientists describe a younger generation as “drunk with code.” Young scientists, engineers, and designers, full citizens of the virtual, scramble to capture their mentors' tacit knowledge of buildings and bodies. From both sides of a generational divide, there is anxiety that in simulation, something important is slipping away. Turkle's examination of simulation over the past twenty years is followed by four in-depth investigations of contemporary simulation culture: space exploration, oceanography, architecture, and biology.




The Complete Guide to Simulations and Serious Games


Book Description

"Ready to blow your mind? Spend 15 seconds reading Clark Aldrich's The Complete Guide to Simulations and Serious Games. Witty, fast-paced, and non-linear -- it's Spock meets Alton Brown." -- Lynne Kenney, Psy.D., The Family Coach This exciting work offers designers a new way to see the world, model it, and present it through simulations. A groundbreaking resource, it includes a wealth of new tools and terms and a corresponding style guide to help understand them. The author -- a globally recognized industry guru -- covers topics such as virtual experiences, games, simulations, educational simulations, social impact games, practiceware, game-based learning/digital game based learning, immersive learning, and serious games. This book is the first of its kind to present definitions of more than 600 simulation and game terms, concepts, and constructs.




Computer Simulation Validation


Book Description

This unique volume introduces and discusses the methods of validating computer simulations in scientific research. The core concepts, strategies, and techniques of validation are explained by an international team of pre-eminent authorities, drawing on expertise from various fields ranging from engineering and the physical sciences to the social sciences and history. The work also offers new and original philosophical perspectives on the validation of simulations. Topics and features: introduces the fundamental concepts and principles related to the validation of computer simulations, and examines philosophical frameworks for thinking about validation; provides an overview of the various strategies and techniques available for validating simulations, as well as the preparatory steps that have to be taken prior to validation; describes commonly used reference points and mathematical frameworks applicable to simulation validation; reviews the legal prescriptions, and the administrative and procedural activities related to simulation validation; presents examples of best practice that demonstrate how methods of validation are applied in various disciplines and with different types of simulation models; covers important practical challenges faced by simulation scientists when applying validation methods and techniques; offers a selection of general philosophical reflections that explore the significance of validation from a broader perspective. This truly interdisciplinary handbook will appeal to a broad audience, from professional scientists spanning all natural and social sciences, to young scholars new to research with computer simulations. Philosophers of science, and methodologists seeking to increase their understanding of simulation validation, will also find much to benefit from in the text.




Discrete Choice Methods with Simulation


Book Description

This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered: logit, generalized extreme value, or GEV (including nested and cross-nested logits), probit, and mixed logit, plus a variety of specifications that build on these basics. Simulation-assisted estimation procedures are investigated and compared, including maximum stimulated likelihood, method of simulated moments, and method of simulated scores. Procedures for drawing from densities are described, including variance reduction techniques such as anithetics and Halton draws. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. The second edition adds chapters on endogeneity and expectation-maximization (EM) algorithms. No other book incorporates all these fields, which have arisen in the past 25 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing.




Practical Simulations for Machine Learning


Book Description

Simulation and synthesis are core parts of the future of AI and machine learning. Consider: programmers, data scientists, and machine learning engineers can create the brain of a self-driving car without the car. Rather than use information from the real world, you can synthesize artificial data using simulations to train traditional machine learning models.That's just the beginning. With this practical book, you'll explore the possibilities of simulation- and synthesis-based machine learning and AI, concentrating on deep reinforcement learning and imitation learning techniques. AI and ML are increasingly data driven, and simulations are a powerful, engaging way to unlock their full potential. You'll learn how to: Design an approach for solving ML and AI problems using simulations with the Unity engine Use a game engine to synthesize images for use as training data Create simulation environments designed for training deep reinforcement learning and imitation learning models Use and apply efficient general-purpose algorithms for simulation-based ML, such as proximal policy optimization Train a variety of ML models using different approaches Enable ML tools to work with industry-standard game development tools, using PyTorch, and the Unity ML-Agents and Perception Toolkits




Demystifying Climate Models


Book Description

This book demystifies the models we use to simulate present and future climates, allowing readers to better understand how to use climate model results. In order to predict the future trajectory of the Earth’s climate, climate-system simulation models are necessary. When and how do we trust climate model predictions? The book offers a framework for answering this question. It provides readers with a basic primer on climate and climate change, and offers non-technical explanations for how climate models are constructed, why they are uncertain, and what level of confidence we should place in them. It presents current results and the key uncertainties concerning them. Uncertainty is not a weakness but understanding uncertainty is a strength and a key part of using any model, including climate models. Case studies of how climate model output has been used and how it might be used in the future are provided. The ultimate goal of this book is to promote a better understanding of the structure and uncertainties of climate models among users, including scientists, engineers and policymakers.




A National Strategy for Advancing Climate Modeling


Book Description

As climate change has pushed climate patterns outside of historic norms, the need for detailed projections is growing across all sectors, including agriculture, insurance, and emergency preparedness planning. A National Strategy for Advancing Climate Modeling emphasizes the needs for climate models to evolve substantially in order to deliver climate projections at the scale and level of detail desired by decision makers, this report finds. Despite much recent progress in developing reliable climate models, there are still efficiencies to be gained across the large and diverse U.S. climate modeling community. Evolving to a more unified climate modeling enterprise-in particular by developing a common software infrastructure shared by all climate researchers and holding an annual climate modeling forum-could help speed progress. Throughout this report, several recommendations and guidelines are outlined to accelerate progress in climate modeling. The U.S. supports several climate models, each conceptually similar but with components assembled with slightly different software and data output standards. If all U.S. climate models employed a single software system, it could simplify testing and migration to new computing hardware, and allow scientists to compare and interchange climate model components, such as land surface or ocean models. A National Strategy for Advancing Climate Modeling recommends an annual U.S. climate modeling forum be held to help bring the nation's diverse modeling communities together with the users of climate data. This would provide climate model data users with an opportunity to learn more about the strengths and limitations of models and provide input to modelers on their needs and provide a venue for discussions of priorities for the national modeling enterprise, and bring disparate climate science communities together to design common modeling experiments. In addition, A National Strategy for Advancing Climate Modeling explains that U.S. climate modelers will need to address an expanding breadth of scientific problems while striving to make predictions and projections more accurate. Progress toward this goal can be made through a combination of increasing model resolution, advances in observations, improved model physics, and more complete representations of the Earth system. To address the computing needs of the climate modeling community, the report suggests a two-pronged approach that involves the continued use and upgrading of existing climate-dedicated computing resources at modeling centers, together with research on how to effectively exploit the more complex computer hardware systems expected over the next 10 to 20 years.