Search results for "Generative AI Modeling"
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Agent-Based Modeling and Simulation with Swarm
Swarm-based multi-agent simulation leads to better modeling of tasks in biology, engineering, economics, art, and many other areas. It also facilitates an understanding of complicated phenomena that cannot be solved analytically. Agent-Based Modeling and Simulation with Swarm provides the methodology for a multi-agent-based modeling approach that integrates computational techniques such as artificial life, cellular automata, and bio-inspired optimization.Each chapter gives an overview of the problem, explores state-of-the-art technology in the field, and discusses multi-agent frameworks. The author describes step by step how to assemble algorithms for generating a simulation model, program, method for visualization, and further research tasks. While the book employs the commonly used Swarm system, readers can model and develop the simulations with their own simulator. To encourage hands-on exploration of emergent systems, Swarm-based software and source codes are available for download from the author’s website. A thorough overview of multi-agent simulation and supporting tools, this book shows how this type of simulation is used to acquire an understanding of complex systems and artificial life. It carefully explains how to construct a simulation program for various applications.
R 10,646.65
AI Powered Digital Twins
Build secure, effective digital twins using proven engineering principles and AI foundations AI Powered Digital Twins: A Guide for Humans, Engineers, and Enterprises provides a unified framework for implementing digital twin systems where AI agents reason, act, and remain governed. Hala Nelson lays out a 12-layer engineering architecture spanning raw data ingestion, semantic spine, event, native core, agent reasoning loops, simulation, Zero Trust security, and governance lifecycle. The architecture targets systems that must perform in critical infrastructure rather than demonstrations. The book delivers a human-centered Standard of Care framework alongside real-world case studies from critical infrastructure and system engineering. Readers gain practical guidance on integrating AI, data engineering, asset tagging, knowledge graphs and quantum computing within digital twin environments. Coverage includes accountability frameworks modeled on the rigor of aviation, healthcare, and civil engineering, plus an AI and data strategy designed to translate directly into measurable business value. The book stresses engineering discipline to bridge the gap between AI investment and real-world delivery. The book contains: How AI agents reason, act, and stay governed within digital twins using Zero Trust security and governance controlsA modular architecture for blending physical and virtual worlds while protecting safety across operational technology environmentsAccountability frameworks modeled on aviation, healthcare, and civil engineering that hold AI systems to a standard of careCase studies demonstrating implementation strategies for manufacturing and integrated system engineeringAn AI and data strategy that translates into measurable business value rather than a policy document on a shelf 17 chapters. One architecture. Zero handwaving about security. A must-read for engineers, architects, technical leaders, strategists, and the policymakers who depend on their judgment.
R 711.00
Modelling and Simulation for Autonomous Systems
This volume constitutes the revised selected papers of the 12th International Conference on Modelling and Simulation for Autonomous Systems, MESAS 2025, held on October 21–23, 2025, in Istanbul, Turkey.The 21 full papers included in these proceedings were carefully reviewed and selected from 32 submissions. They were organized in the following topical sections:AxS/AI in Context of Future Warfare and Security Environment (Concepts, Applications, Standards, etc.); M&S of Intelligent Systems – R&D and Application; Future Challenges of Advanced M&S Technology.
R 2,950.70
Hands-On Simulation Modeling with Python
Enhance your simulation modeling skills by creating and analyzing digital prototypes of a physical model using Python programming with this comprehensive guideKey FeaturesLearn to create a digital prototype of a real model using hands-on examplesEvaluate the performance and output of your prototype using simulation modeling techniquesUnderstand various statistical and physical simulations to improve systems using PythonBook DescriptionSimulation modeling helps you to create digital prototypes of physical models to analyze how they work and predict their performance in the real world. With this comprehensive guide, you'll understand various computational statistical simulations using Python.Starting with the fundamentals of simulation modeling, you'll understand concepts such as randomness and explore data generating processes, resampling methods, and bootstrapping techniques. You'll then cover key algorithms such as Monte Carlo simulations and Markov decision processes, which are used to develop numerical simulation models, and discover how they can be used to solve real-world problems. As you advance, you'll develop simulation models to help you get accurate results and enhance decision-making processes. Using optimization techniques, you'll learn to modify the performance of a model to improve results and make optimal use of resources. The book will guide you in creating a digital prototype using practical use cases for financial engineering, prototyping project management to improve planning, and simulating physical phenomena using neural networks.By the end of this book, you'll have learned how to construct and deploy simulation models of your own to overcome real-world challenges.What you will learnGain an overview of the different types of simulation modelsGet to grips with the concepts of randomness and data generation processUnderstand how to work with discrete and continuous distributionsWork with Monte Carlo simulations to calculate a definite integralFind out how to simulate random walks using Markov chainsObtain robust estimates of confidence intervals and standard errors of population parametersDiscover how to use optimization methods in real-life applicationsRun efficient simulations to analyze real-world systemsWho this book is forHands-On Simulation Modeling with Python is for simulation developers and engineers, model designers, and anyone already familiar with the basic computational methods that are used to study the behavior of systems. This book will help you explore advanced simulation techniques such as Monte Carlo methods, statistical simulations, and much more using Python. Working knowledge of Python programming language is required.
R 2,214.00
Hands-On Simulation Modeling with Python
Learn to construct state-of-the-art simulation models with Python and enhance your simulation modelling skills, as well as create and analyze digital prototypes of physical models with easeKey FeaturesUnderstand various statistical and physical simulations to improve systems using PythonLearn to create the numerical prototype of a real model using hands-on examplesEvaluate performance and output results based on how the prototype would work in the real worldBook DescriptionSimulation modelling is an exploration method that aims to imitate physical systems in a virtual environment and retrieve useful statistical inferences from it. The ability to analyze the model as it runs sets simulation modelling apart from other methods used in conventional analyses. This book is your comprehensive and hands-on guide to understanding various computational statistical simulations using Python. The book begins by helping you get familiarized with the fundamental concepts of simulation modelling, that’ll enable you to understand the various methods and techniques needed to explore complex topics. Data scientists working with simulation models will be able to put their knowledge to work with this practical guide. As you advance, you’ll dive deep into numerical simulation algorithms, including an overview of relevant applications, with the help of real-world use cases and practical examples. You'll also find out how to use Python to develop simulation models and how to use several Python packages. Finally, you’ll get to grips with various numerical simulation algorithms and concepts, such as Markov Decision Processes, Monte Carlo methods, and bootstrapping techniques.By the end of this book, you'll have learned how to construct and deploy simulation models of your own to overcome real-world challenges.What you will learnGet to grips with the concept of randomness and the data generation processDelve into resampling methodsDiscover how to work with Monte Carlo simulationsUtilize simulations to improve or optimize systemsFind out how to run efficient simulations to analyze real-world systemsUnderstand how to simulate random walks using Markov chainsWho this book is forThis book is for data scientists, simulation engineers, and anyone who is already familiar with the basic computational methods and wants to implement various simulation techniques such as Monte-Carlo methods and statistical simulation using Python.
R 1,693.00
Discrete Event Modeling and Simulation Technologies
During the 1990s the computing industry has witnessed many advances in mobile and enterprise computing. Many of these advances have been made possible by developments in the areas such as modeling, simulation, and artificial intelligence. Within the different areas of enterprise computing - such as manufacturing, health organisation, and commerce - the need for a disciplined, multifaceted, and unified approach to modeling and simulation has become essential. This new book provides a forum for scientists, academics, and professionals to present their latest research findings from the various fields: artificial intelligence, collaborative/distributed computing, modeling, and simulation.
R 1,635.48
