Machine Learning in the Analysis of Solid Deformation, Fatigue and Fracture fills a clear gap in literature by applying machine learning to deformation, fatigue, and fracture analysis in solid mechanics. The book’s focus on complex mechanisms and coupling phenomena, discussed with practical examples, makes it a valuable resource for advanced researchers. Practical examples and case studies enable readers to understand both the underlying engineering problems and the application of machine learning methods to enhance fatigue life prediction analysis for solid materials and structures. Provides a systematic summary of machine learning methods applied to solid deformation, fatigue and fracture analysis Fills a clear gap in the literature by applying machine learning to deformation, fatigue, and fracture analysis in solid mechanics Introduces the application of physics-informed machine learning in multiaxial fatigue life prediction Introduces the application of physics-informed machine learning in predicting the fatigue life of additively manufactured metallic metals Includes numerous practical examples and case studies and draws on the authors’ extensive experience in multiscale simulation of solid materials and fatigue life prediction