9783527355532 - constitutive models of solid materials: from mechanical principles to engineering applications di yao, yao; fang, hu; guo, hongcun; zeng, tao; he, xu (8 risultati)
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Buch. Condizione: Neu. Neuware - Covers a wide range of essential concepts and skills, from continuum mechanics to advanced constitutive modeling, addressing the needs of both academics and professionals.
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Hardcover. Condizione: new. Hardcover. 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 Eshelbys theory, Hills 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Hardcover. Condizione: new. Hardcover. 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 Eshelbys theory, Hills 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Da: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
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Hardcover. Condizione: new. Hardcover. 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 Eshelbys theory, Hills 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. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
