Through decades of use and refinement, endovascular stents have become part and parcel of the management of obstructive atherosclerotic lesions. Upon stent placement, a variety of biophysical reactions ensue, governed not only by the mechanical and material properties of the device, but also the impact these properties have on the local vascular biology. Anatomic changes and vascular deformations give rise to solid mechanical and fluid forces that are the proximate, functional drivers of the induced reparative response. Powerful computational tools and advanced imaging techniques allow us to define these forces with high precision and increasingly, at a patient-specific level. We have also gained fundamental insights into how these forces influence subcellular and cellular processes, and, through application of a variety of model systems, how they subsequently drive an integrated tissue response. Clinical studies extend understanding to actual patients and pathophysiologic scenarios. These tools and insights take on added weight given the real risks that accompany the many substantial benefits of stenting. Complex lesions remain difficult to manage and continue to be associated with worse outcomes. While many patients respond well to treatment, others suffer treatment failures and recurrent events – sometimes catastrophic. Overcoming such variability requires that we move towards individualized treatment plans. Doing so necessitates that we develop not just a qualitative understanding of involved phenomena, but a quantitative ability to predict integrated outcomes. Given the multi-scale nature of the vascular response to stenting, it is critical that models, be they computational, bench-top, animal, or clinical, can be verified, validated, and made interrelated. This review provides an overview of the biophysics governing endovascular stenting, their integration in real-world endovascular settings, and how simulation and statistical approaches are helping to bridge the gap between qualitative model understanding and quantitative clinical prediction.