Aortic aneurysms and dissections require personalized therapeutic strategies to reduce the risk of catastrophic complications. This lecture introduces a multiscale digital-twin framework combining fast surrogate biomechanical models, machine-learning–based inverse identification, and tissue mechanobiology. At the clinical scale, latent-space representations enable rapid patient-specific simulation to predict complications such as endoleaks. At the material scale, inverse methods and neural-network constitutive models reveal regional tissue properties and rupture indicators. At the biological scale, growth-and-remodeling models describe how proteolytic injury, hemodynamics, and cellular processes drive disease progression. By integrating these components, digital twins become powerful tools to support diagnosis, intervention planning, and future therapies for delaying vascular aging.