This book is an introduction to the use of machine learning and data-driven approaches in fluid simulation and animation, as an alternative to traditional modeling techniques based on partial differential equations and numerical methods – and at a lower computational cost. This work starts with a brief review of computability theory, aimed to convince the reader – more specifically, researchers of more traditional areas of mathematical modeling – about the power of neural computing in fluid animations. In these initial chapters, fluid modeling through Navier-Stokes equations and numerical methods are also discussed. The following chapters explore the advantages of the neural networks approach and show the building blocks of neural networks for fluid simulation. They cover aspects related to training data, data augmentation, and testing. The volume completes with two case studies, one involving Lagrangian simulation of fluids using convolutional neural networks and the other using Generative Adversarial Networks (GANs) approaches.

This book presents techniques for creating fluid-like animations with no required advanced physics and mathematical skills. It describes how to create fluid animations like water, smoke, fire, and explosions through computer code in a fun manner. It includes a historical background of the computation of fluids as well as concepts that drive fluid animations, and also provides computer code that readers can download and run on several platforms to create their own programs using fluid animation.

A practical introduction, the second edition of Fluid Simulation for Computer Graphics shows you how to animate fully three-dimensional incompressible flow. It covers all the aspects of fluid simulation, from the mathematics and algorithms to implementation, while making revisions and updates to reflect changes in the field since the first edition. Highlights of the Second Edition New chapters on level sets and vortex methods Emphasizes hybrid particle–voxel methods, now the industry standard approach Covers the latest algorithms and techniques, including: fluid surface reconstruction from particles; accurate, viscous free surfaces for buckling, coiling, and rotating liquids; and enhanced turbulence for smoke animation Adds new discussions on meshing, particles, and vortex methods The book changes the order of topics as they appeared in the first edition to make more sense when reading the first time through. It also contains several updates by distilling author Robert Bridson’s experience in the visual effects industry to highlight the most important points in fluid simulation. It gives you an understanding of how the components of fluid simulation work as well as the tools for creating your own animations.

Over the last decade, progress in deep learning has had a profound and transformational effect on many complex problems, including speech recognition, machine translation, natural language understanding, and computer vision. As a result, computers can now achieve human-competitive performance in a wide range of perception and recognition tasks. Many of these systems are now available to the programmer via a range of so-called cognitive services. More recently, deep reinforcement learning has achieved ground-breaking success in several complex challenges. This book makes an enormous contribution to this beautiful, vibrant area of study: an area that is developing rapidly both in breadth and depth. Deep learning can cope with a broader range of tasks (and perform those tasks to increasing levels of excellence). This book lays a good foundation for the core concepts and principles of deep learning in gaming and animation, walking you through the fundamental ideas with expert ease. This book progresses in a step-by-step manner. It reinforces theory with a full-fledged pedagogy designed to enhance students' understanding and offer them a practical insight into its applications. Also, some chapters introduce and cover novel ideas about how artificial intelligence (AI), deep learning, and machine learning have changed the world in gaming and animation. It gives us the idea that AI can also be applied in gaming, and there are limited textbooks in this area. This book comprehensively addresses all the aspects of AI and deep learning in gaming. Also, each chapter follows a similar structure so that students, teachers, and industry experts can orientate themselves within the text. There are few books in the field of gaming using AI. Deep Learning in Gaming and Animations teaches you how to apply the power of deep learning to build complex reasoning tasks. After being exposed to the foundations of machine and deep learning, you will use Python to build a bot and then teach it the game's rules. This book also focuses on how different technologies have revolutionized gaming and animation with various illustrations.

Simulating and visualizing fluid and solid materials in agricultural domains is an important and challenging problem in scientific computing and computer vision. Modern seed breeding programs require the ability to analyze seeds efficiently to be useful. Even simple measures such as volume and density can be challenging to compute efficiently with modest equipment. The dynamics of liquid and soil materials involve significant deformation during storm flows and require sophisticated numerical algorithms to achieve sufficient accuracy and visual realism. This dissertation focuses on extending volume carving techniques to measure seed volume and to create a new Material Point Method (MPM) models and finite volume models to simulate solids and fluids for dam safety analysis and visualization. This dissertation makes the following major contributions: The first is to create a novel framework for the design and analysis of computer experiments. The framework is applied to perform efficient dam breach and internal erosion analysis on a large number of structures. Given historical dam breach or design data input, the modeling framework can also be used to conduct sensitivity analysis to determine which parameters make the most impact on the resulting dam erosion. The second contribution is to develop new models for numerical simulation of dam erosion by combining fluid flow models developed using Computational Fluid Dynamics (CFD) with new dam erosion models using the Finite Element Method (FEM). A new model that combines fluid flow and erosion simulation into a single model is also developed using the Material Point Method (MPM). The third contribution is to build a comprehensive image capture and processing framework for seed property analysis. Rather than having a human manually measure seed properties such as length, width, thickness, and volume, the framework can automatically analyze a set of images from multiple angles and calculate the physical measurements for single seed samples. Finally, image analysis is extended using deep learning to increase the accuracy of rice image classification. The proposed frameworks are suitable for larger scale and more dynamic data in both dam safety and agricultural domains. They are also useful for computer animation in developing physics-based special effects for the animation of dam erosion. Previous work on MPM has resulted in models used in animation for Disney Studios, and the new models proposed could be used for accurate animation of fluid flows and dam erosion. Finally, the combination of image analysis algorithms and deep learning has many applications in the biomedical domain as well as the agricultural domain.

From the splash of breaking waves to turbulent swirling smoke, the mathematical dynamics of fluids are varied and continue to be one of the most challenging aspects in animation. Fluid Engine Development demonstrates how to create a working fluid engine through the use of particles and grids, and even a combination of the two. Core algorithms are explained from a developer’s perspective in a practical, approachable way that will not overwhelm readers. The Code Repository offers further opportunity for growth and discussion with continuously changing content and source codes. This book helps to serve as the ultimate guide to navigating complex fluid animation and development. Explains how to create a fluid simulation engine from scratch Offers an approach that is code-oriented rather than math-oriented, allowing readers to learn how fluid dynamics works with code, with downloadable code available Explores various kinds of simulation techniques for fluids using particles and grids Discusses practical issues such as data structure design and optimizations Covers core numerical tools including linear system and level set solvers