Timon Rabczuk – författare

Extended Finite Element and Meshfree Methods provides an overview of, and investigates, recent developments in extended finite elements with a focus on applications to material failure in statics and dynamics. This class of methods is ideally suited for applications, such as crack propagation, two-phase flow, fluid-structure-interaction, optimization and inverse analysis because they do not require any remeshing. These methods include the original extended finite element method, smoothed extended finite element method (XFEM), phantom node method, extended meshfree methods, numerical manifold method and extended isogeometric analysis.

This book also addresses their implementation and provides small MATLAB codes on each sub-topic. Also discussed are the challenges and efficient algorithms for tracking the crack path which plays an important role for complex engineering applications.

Explains all the important theory behind XFEM and meshfree methods Provides advice on how to implement XFEM for a range of practical purposes, along with helpful MATLAB codes Draws on the latest research to explore new topics, such as the applications of XFEM to shell formulations, and extended meshfree and extended isogeometric methods Introduces alternative modeling methods to help readers decide what is most appropriate for their work

Extended Finite Element and Meshfree Methods provides an overview of, and investigates, recent developments in extended finite elements with a focus on applications to material failure in statics and dynamics. This class of methods is ideally suited for applications, such as crack propagation, two-phase flow, fluid-structure-interaction, optimization and inverse analysis because they do not require any remeshing. These methods include the original extended finite element method, smoothed extended finite element method (XFEM), phantom node method, extended meshfree methods, numerical manifold method and extended isogeometric analysis.This book also addresses their implementation and provides small MATLAB codes on each sub-topic. Also discussed are the challenges and efficient algorithms for tracking the crack path which plays an important role for complex engineering applications.- Explains all the important theory behind XFEM and meshfree methods- Provides advice on how to implement XFEM for a range of practical purposes, along with helpful MATLAB codes- Draws on the latest research to explore new topics, such as the applications of XFEM to shell formulations, and extended meshfree and extended isogeometric methods- Introduces alternative modeling methods to help readers decide what is most appropriate for their work

This book is a collection of new artificial intelligence methods, including new regression, machine learning, deep learning, hybrid, and ensemble models, published in recent years. The algorithms and structures of these methods are clearly explained such that the readers can use them in their studies and projects. In addition, by presenting several examples for models, the strengths and weaknesses of each model are explained to help the readers to select the best model for each kind of dataset. Therefore, the readers can purposefully and usefully select the appropriate models among all models. As a result, the first advantage of this book in comparison to other books refers to presentation of the new AI models in details, comparison them with respect to their capability and feature, and some criteria to select the appropriate model for the various dataset. Moreover, this book provides the required information on sustainable development, which the students need to know, as well as the advanced information and details for using new AI approaches for sustainable development goals.To improve the criterion in the sustainable development, management of the current and future situation is one the important factors to have better situation with respect to sustainability, which function needs the prediction approaches for future; for example, for environmental indicators, like the management of air pollution, climate change, waste, and energy consumption. Als, the sustainability has other variables, such as social, technical, and economic indicators that to management of these indicators, the new AI can efficiently be used. Furthermore, the new AI can increase the number of models and estimate more indicators in multi-criteria decision analysis; therefore, the accuracy of this type of analyses to have a sustainable production and development can improve regarding an increase in the number of options and indicators, which the cost and time of analyses will be decreased using the AI methods. Briefly, this book provides many new stronger AI approaches for the various studies in sustainable development that are very interesting for the researchers and students.

This book provides an overview of computational methods based on peridynamics and nonlocal operators and their application to challenging numerical problems which are difficult to deal with traditional methods such as the finite element method, material failure being “only” one of them. The authors have also developed a higher-order nonlocal operator approaches capable of solving higher-order partial differential equations on arbitrary domains in higher-dimensional space with ease. This book is of interest to those in academia and industry.

This book provides an overview of computational methods based on peridynamics and nonlocal operators and their application to challenging numerical problems which are difficult to deal with traditional methods such as the finite element method, material failure being “only” one of them. The authors have also developed a higher-order nonlocal operator approaches capable of solving higher-order partial differential equations on arbitrary domains in higher-dimensional space with ease. This book is of interest to those in academia and industry. 

 

This book provides an overview of computational methods based on peridynamics and nonlocal operators and their application to challenging numerical problems which are difficult to deal with traditional methods such as the finite element method, material failure being “only” one of them. The authors have also developed a higher-order nonlocal operator approaches capable of solving higher-order partial differential equations on arbitrary domains in higher-dimensional space with ease. This book is of interest to those in academia and industry.

Machine learning (ML)  approaches have been extensively and successfully employed in various areas, like in economics, medical predictions,  face recognition, credit card fraud detection, and spam filtering. There is clearly also the potential that ML techniques developed in Engineering and the Sciences will drastically increase the possibilities of analysis and accelerate the design to analysis time.  With the use of ML techniques, coupled to conventional methods like finite element and digital twin technologies, new avenues of  modeling and simulation can be opened but the potential of these ML techniques needs to still be fully harvested, with the methods developed and enhanced. The objective of this book is to provide an overview of ML in Engineering and the Sciences presenting fundamental theoretical ingredients with a focus on the next generation of computer modeling in Engineering and the Sciences in which the exciting aspects of machine learning are incorporated. The book is of value to any researcher and practitioner interested in research or applications of ML in the areas of scientific modeling and computer aided engineering.

Machine learning (ML)  approaches have been extensively and successfully employed in various areas, like in economics, medical predictions,  face recognition, credit card fraud detection, and spam filtering. There is clearly also the potential that ML techniques developed in Engineering and the Sciences will drastically increase the possibilities of analysis and accelerate the design to analysis time.  With the use of ML techniques, coupled to conventional methods like finite element and digital twin technologies, new avenues of  modeling and simulation can be opened but the potential of these ML techniques needs to still be fully harvested, with the methods developed and enhanced. The objective of this book is to provide an overview of ML in Engineering and the Sciences presenting fundamental theoretical ingredients with a focus on the next generation of computer modeling in Engineering and the Sciences in which the exciting aspects of machine learning are incorporated. The book is of value to any researcher and practitioner interested in research or applications of ML in the areas of scientific modeling and computer aided engineering.

Machine learning (ML)  approaches have been extensively and successfully employed in various areas, like in economics, medical predictions,  face recognition, credit card fraud detection, and spam filtering. There is clearly also the potential that ML techniques developed in Engineering and the Sciences will drastically increase the possibilities of analysis and accelerate the design to analysis time.  With the use of ML techniques, coupled to conventional methods like finite element and digital twin technologies, new avenues of  modeling and simulation can be opened but the potential of these ML techniques needs to still be fully harvested, with the methods developed and enhanced. The objective of this book is to provide an overview of ML in Engineering and the Sciences presenting fundamental theoretical ingredients with a focus on the next generation of computer modeling in Engineering and the Sciences in which the exciting aspects of machine learning are incorporated. The book is of value to any researcher and practitioner interested in research or applications of ML in the areas of scientific modeling and computer aided engineering.

This book equips readers with a rigorous and practical framework for solving complex engineering problems directly from governing equations using modern machine learning techniques. It bridges established principles from mechanics, numerical analysis, and scientific computing with emerging physics-based learning approaches, enabling reliable modeling, simulation, optimization, and inverse analysis beyond purely data-driven methods. A distinctive feature is its critical comparison of machine learning-based solvers with classical techniques such as the finite element method, isogeometric analysis, and meshfree methods, highlighting strengths, limitations, and domains of applicability. The scope ranges from foundational concepts to advanced engineering applications, supported by worked examples, reproducible code, and extensive references. The book is intended for graduate students, researchers, and practitioners in engineering, applied mathematics, and computational sciences who seek a principled entry point and a state-of-the-art reference for physics-based machine learning in modeling and simulation.

This book equips readers with a rigorous and practical framework for solving complex engineering problems directly from governing equations using modern machine learning techniques. It bridges established principles from mechanics, numerical analysis, and scientific computing with emerging physics-based learning approaches, enabling reliable modeling, simulation, optimization, and inverse analysis beyond purely data-driven methods. A distinctive feature is its critical comparison of machine learning-based solvers with classical techniques such as the finite element method, isogeometric analysis, and meshfree methods, highlighting strengths, limitations, and domains of applicability. The scope ranges from foundational concepts to advanced engineering applications, supported by worked examples, reproducible code, and extensive references. The book is intended for graduate students, researchers, and practitioners in engineering, applied mathematics, and computational sciences who seek a principled entry point and a state-of-the-art reference for physics-based machine learning in modeling and simulation.

This book provides an overview of state-of-the-art methods in computational engineering for modeling and simulation.This proceedings volume includes a selection of refereed papers presented at the International Conference on Advances in Computational Mechanics (ACOME) 2017, which took place on Phu Quoc Island, Vietnam on August 2-4, 2017.The contributions highlight recent advances in and innovative applications of computational mechanics. Subjects covered include: biological systems; damage, fracture and failure; flow problems; multiscale multiphysics problems; composites and hybrid structures; optimization and inverse problems; lightweight structures; computational mechatronics; computational dynamics; numerical methods; and high-performance computing.The book is intended for academics, including graduate students and experienced researchers interested in state-of-the-art computational methods for solving challenging problems in engineering.