Peridynamic Differential Operator for Numerical Analysis

Discover the groundbreaking insights of Peridynamic Differential Operator for Numerical Analysis by Erdogan Madenci, published by Springer Nature Switzerland AG in 2019. This comprehensive hardback edition spans 282 pages and delves into the innovative Peridynamic (PD) concept, which serves as a powerful tool for smoothing noisy data and recovering missing information.

The book begins with a thorough overview of the PD concept, followed by a detailed derivation of the PD differential operator. It also covers the numerical implementation of spatial and temporal derivatives, providing readers with essential knowledge to understand the sources of error in these processes. Whether you are a researcher, practitioner, or student in the field of numerical analysis, this book is an invaluable resource that enhances your understanding of PD and its applications.