Fundamentals of Uncertainty Quantification for Engineers

Fundamentals of Uncertainty Quantification for Engineers by Yan Wang, Anh V. Tran, and David L. McDowell is an essential resource for engineers and researchers who need to turn uncertain data into confident decisions. Clear, rigorous, and application-driven, this book demystifies probabilistic modeling, sensitivity analysis, stochastic simulation, and model calibration with engineering-grade examples.

Begin thinking differently about risk and reliability as the authors guide you through foundational theory and practical techniques. You’ll find accessible explanations of Bayesian inference, Monte Carlo methods, surrogate modeling, and uncertainty propagation—each presented with real-world engineering scenarios spanning mechanical, civil, aerospace, and electrical contexts. Emphasis on numerical methods ensures the content is immediately actionable for design optimization, materials modeling, and system-level risk assessment.

Designed for graduate students, practicing engineers, and technical managers, this title bridges mathematics and practice. Readers gain tools to quantify input variability, assess model confidence, and communicate uncertainty to stakeholders—skills that improve safety, performance, and regulatory compliance. Comprehensive yet approachable, the book supports coursework and professional projects alike.

Whether you’re teaching a course in uncertainty quantification or implementing probabilistic workflows in industry, this book provides a modern, global perspective tailored to engineers in North America, Europe, Asia, and beyond. Add Fundamentals of Uncertainty Quantification for Engineers to your library to sharpen decision-making under uncertainty—order your copy today and start building more reliable, resilient engineering solutions.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.