Predictive modeling of blood flow and pressure have numerous applications ranging from non-invasive assessment of functional significance of disease to planning invasive procedures. While several such predictive modeling techniques have been proposed, their use in the clinic has been limited due in part to the significant time required to perform virtual interventions and compute the resultant changes in hemodynamic conditions. We propose a fast hemodynamic assessment method to aid in interventional planning based on first constructing an exploration space of geometries, tailored to each patient, and subsequently building a physics driven reduced order model in this space. We demonstrate that this method can predict fractional flow reserve derived from coronary computed tomography angiography in response to changes to a patient-specific lumen geometry in real time while achieving high accuracy when compared to computational fluid dynamics simulations. We validated this method on over 1300 patients that received a coronary CT scan and demonstrated a correlation coefficient of 0.98 with an error of 0.005 +/- 0.015 (95% CI: (-0.020, 0.031)) as compared to three-dimensional blood flow calculations. This technology is implemented in a product that has received clearance by the U.S. Food and Drug Administration and is being used clinically to enable physicians to predict changes in blood flow resulting from removal of coronary stenoses as might occur with percutaneous coronary interventions. This technology is also cleared for use in Japan and pending regulatory approval in Europe. (C) 2020 Elsevier B.V. All rights reserved.