Tao Zeng – författare

Financial Econometrics is a contribution to modern financial econometrics, overviewing both theory and application. It covers, in detail, three important topics in the field that have recently drawn the attention of the academic community and practitioners, with low-frequency data (trend determination, bubble detection, and factor-augmented regressions) and examines various topics in high-frequency financial econometrics with continuous time models and discretized data. Also included are the estimation of stochastic volatility models, posterior-based hypothesis testing, and posterior-based model selection. Exploring topics at the forefront of research in the field of financial econometrics, this book offers an accessible introduction to the research and provides the groundwork for the development of new econometric techniques.

Explore constitutive modeling from fundamental theory through cutting-edge AI applications Constitutive Models of Solid Materials: From Mechanical Principles to Engineering Applications provides researchers and engineers with comprehensive methods to predict material responses under complex loading conditions. Written by internationally recognized experts in materials mechanics and computational methods, this systematic treatment connects rigorous theoretical foundations to practical engineering applications, demonstrating the accurate modeling of material behavior across multiple scales. The text progresses methodically from tensor analysis and continuum mechanics through elasticity, plasticity, and damage mechanics to micromechanics and numerical implementation strategies. Coverage extends to artificial intelligence integration in constitutive research, featuring Physics-Informed Neural Networks for constitutive parameter prediction. Four detailed case studies examine sintered nano-silver, high-strength steel, solder alloys, rock modeling, and high-temperature concrete performance. The book offers: Comprehensive coverage from mathematical foundations through elastoplastic theory, damage mechanics, and micromechanics to AI-enhanced modeling approachesNumerical implementation strategies including time-stepping schemes, Newton-Raphson iteration, and elastic predictor plastic corrector methods for simulationsDetailed case studies on sintered nano-silver, high-strength steels, solder alloys, rocks, and concrete under extreme conditionsIntegration of machine learning including Artificial Neural Networks, XGBoost, and Physics-Informed Neural Networks with example programsMultiscale frameworks combining Eshelby’s theory, Hill’s method, and homogenization techniques linking microstructure to macroscopic behaviorThis comprehensive resource serves materials scientists, mechanical engineers, civil engineers, aerospace professionals, and graduate students seeking to master constitutive modeling. By combining rigorous mathematical formulation with computational methods and practical case studies, it provides an essential foundation for advanced materials research and engineering practice.

Explore constitutive modeling from fundamental theory through cutting-edge AI applications Constitutive Models of Solid Materials: From Mechanical Principles to Engineering Applications provides researchers and engineers with comprehensive methods to predict material responses under complex loading conditions. Written by internationally recognized experts in materials mechanics and computational methods, this systematic treatment connects rigorous theoretical foundations to practical engineering applications, demonstrating the accurate modeling of material behavior across multiple scales. The text progresses methodically from tensor analysis and continuum mechanics through elasticity, plasticity, and damage mechanics to micromechanics and numerical implementation strategies. Coverage extends to artificial intelligence integration in constitutive research, featuring Physics-Informed Neural Networks for constitutive parameter prediction. Four detailed case studies examine sintered nano-silver, high-strength steel, solder alloys, rock modeling, and high-temperature concrete performance. The book offers: Comprehensive coverage from mathematical foundations through elastoplastic theory, damage mechanics, and micromechanics to AI-enhanced modeling approaches Numerical implementation strategies including time-stepping schemes, Newton-Raphson iteration, and elastic predictor plastic corrector methods for simulations Detailed case studies on sintered nano-silver, high-strength steels, solder alloys, rocks, and concrete under extreme conditions Integration of machine learning including Artificial Neural Networks, XGBoost, and Physics-Informed Neural Networks with example programs Multiscale frameworks combining Eshelby s theory, Hill s method, and homogenization techniques linking microstructure to macroscopic behavior This comprehensive resource serves materials scientists, mechanical engineers, civil engineers, aerospace professionals, and graduate students seeking to master constitutive modeling. By combining rigorous mathematical formulation with computational methods and practical case studies, it provides an essential foundation for advanced materials research and engineering practice.

Explore constitutive modeling from fundamental theory through cutting-edge AI applications Constitutive Models of Solid Materials: From Mechanical Principles to Engineering Applications provides researchers and engineers with comprehensive methods to predict material responses under complex loading conditions. Written by internationally recognized experts in materials mechanics and computational methods, this systematic treatment connects rigorous theoretical foundations to practical engineering applications, demonstrating the accurate modeling of material behavior across multiple scales. The text progresses methodically from tensor analysis and continuum mechanics through elasticity, plasticity, and damage mechanics to micromechanics and numerical implementation strategies. Coverage extends to artificial intelligence integration in constitutive research, featuring Physics-Informed Neural Networks for constitutive parameter prediction. Four detailed case studies examine sintered nano-silver, high-strength steel, solder alloys, rock modeling, and high-temperature concrete performance. The book offers: Comprehensive coverage from mathematical foundations through elastoplastic theory, damage mechanics, and micromechanics to AI-enhanced modeling approaches Numerical implementation strategies including time-stepping schemes, Newton-Raphson iteration, and elastic predictor plastic corrector methods for simulations Detailed case studies on sintered nano-silver, high-strength steels, solder alloys, rocks, and concrete under extreme conditions Integration of machine learning including Artificial Neural Networks, XGBoost, and Physics-Informed Neural Networks with example programs Multiscale frameworks combining Eshelby s theory, Hill s method, and homogenization techniques linking microstructure to macroscopic behavior This comprehensive resource serves materials scientists, mechanical engineers, civil engineers, aerospace professionals, and graduate students seeking to master constitutive modeling. By combining rigorous mathematical formulation with computational methods and practical case studies, it provides an essential foundation for advanced materials research and engineering practice.

This book chiefly addresses the analysis and design of geosynchronous synthetic aperture radar (GEO SAR) systems, focusing on the algorithms, analysis, methods used to compensate for ionospheric influences, and validation experiments for Global Navigation Satellite Systems (GNSS). Further, it investigates special problems in the GEO SAR context, such as curved trajectories, the Earth’s rotation, the ‘non-stop-and-go’ model, high-order Doppler parameters, temporal-variant ionospheric errors etc. These studies can also be extended to SAR with very high resolution and long integration time. Given the breadth and depth of its coverage, scientists and engineers in SAR and advanced graduate students in related areas will greatly benefit from this book.

This book chiefly addresses the analysis and design of geosynchronous synthetic aperture radar (GEO SAR) systems, focusing on the algorithms, analysis, methods used to compensate for ionospheric influences, and validation experiments for Global Navigation Satellite Systems (GNSS). Further, it investigates special problems in the GEO SAR context, such as curved trajectories, the Earth’s rotation, the ‘non-stop-and-go’ model, high-order Doppler parameters, temporal-variant ionospheric errors etc. These studies can also be extended to SAR with very high resolution and long integration time. Given the breadth and depth of its coverage, scientists and engineers in SAR and advanced graduate students in related areas will greatly benefit from this book.