Chapter 1. Co-development of Methodology, Applications,and Hardware in Computational Science and Artificial Intelligence​.- Chapter 2. Novel Strategies for Data-driven Evolutionary Optimization.- Chapter 3. Artificial Intelligence and Computational Science.- Chapter 4. Supervised Learning and Applied Mathematics.- Chapter 5. Application of the Topological Gradient to Parsimonious Neural Networks.- Chapter 6. Generation of Error Indicators for Partial Differential Equations by Machine Learning Methods.- Chapter 7. Newton Method for Minimal Learning Machine.

This book is addressed to young researchers and engineers in the fields of Computational Science and Artificial Intelligence, ranging from innovative computational methods to digital machine learning tools and their coupling used for solving challenging industrial and societal problems.This book provides the latest knowledge from jointly academic and industries experts in Computational Science and Artificial Intelligence fields for exploring possibilities and identifying challenges of applying Computational Sciences and AI methods and tools in industrial and societal sectors.​

Reports support vector machines in clusterwise linear regression Presents similarity solutions for condensation on a non-isothermal vertical plate Provides a second-order method with enriched hessian information for composite sparse optimization problems

GPSR Compliance The European Union's (EU) General Product Safety Regulation (GPSR) is a set of rules that requires consumer products to be safe and our obligations to ensure this. If you have any concerns about our products you can contact us on ProductSafety@springernature.com. In case Publisher is established outside the EU, the EU authorized representative is: Springer Nature Customer Service Center GmbH Europaplatz 3 69115 Heidelberg, Germany ProductSafety@springernature.com