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Scientific modeling books focus on how scientists and engineers build, analyze, and apply abstract representations of real-world systems—ranging from mathematical equations to computational simulations—to explain phenomena, make predictions, and inform decisions. This category covers core principles (abstraction, assumptions, uncertainty), methods and formalisms (differential equations, dynamical systems, statistical and Bayesian modeling, agent-based and network models, machine learning), and practical techniques (calibration, validation, sensitivity analysis, uncertainty quantification). It is inherently interdisciplinary, featuring applications across physics and engineering, biology and medicine, climate and Earth systems, economics, and the social sciences. Titles span accessible primers and course texts to advanced monographs and case-study handbooks for students, researchers, and professionals.

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