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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.
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.
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.
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 Di erential Equations by Machine Learning Methods.- Chapter 7. Newton Method for Minimal Learning Machine.
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.
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 Di erential Equations by Machine Learning Methods.- Chapter 7. Newton Method for Minimal Learning Machine.
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