{ "item_title" : "Uncertainty Quantification of Stochastic Defects in Materials", "item_author" : [" Liu Chu "], "item_description" : "Uncertainty Quantification of Stochastic Defects in Materials investigates the uncertainty quantification methods for stochastic defects in material microstructures. It provides effective supplementary approaches for conventional experimental observation with the consideration of stochastic factors and uncertainty propagation. Pursuing a comprehensive numerical analytical system, this book establishes a fundamental framework for this topic, while emphasizing the importance of stochastic and uncertainty quantification analysis and the significant influence of microstructure defects on the material macro properties.Key Features Consists of two parts: one exploring methods and theories and the other detailing related examples Defines stochastic defects in materials and presents the uncertainty quantification for defect location, size, geometrical configuration, and instability Introduces general Monte Carlo methods, polynomial chaos expansion, stochastic finite element methods, and machine learning methods Provides a variety of examples to support the introduced methods and theories Applicable to MATLAB(R) and ANSYS software This book is intended for advanced students interested in material defect quantification methods and material reliability assessment, researchers investigating artificial material microstructure optimization, and engineers working on defect influence analysis and nondestructive defect testing.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/1/03/212/873/1032128739_b.jpg", "price_data" : { "retail_price" : "233.99", "online_price" : "233.99", "our_price" : "233.99", "club_price" : "233.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }

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