This study uses a one dimensional fluid dynamics arterial network model to infer changes in hemodynamic quantities associated with pulmonary hypertension in mice. Data for this study include blood flow and pressure measurements from the main pulmonary artery for 7 control mice with normal pulmonary function and 5 hypertensive mice with hypoxia induced pulmonary hypertension. Arterial dimensions for a 21 vessel network are extracted from micro-CT images of lungs from a representative control and hypertensive mouse. Each vessel is represented by its length and radius. Fluid dynamic computations are done assuming that the flow is Newtonian, viscous, laminar, and has no swirl. The system of equations is closed by a constitutive equation relating pressure and area, using a linear model derived from stress-strain deformation in the circumferential direction assuming that the arterial walls are thin, and also an empirical nonlinear model. For each dataset, an inflow waveform is extracted from the data, and nominal parameters specifying the outflow boundary conditions are computed from mean values and characteristic time scales extracted from the data. The model is calibrated for each mouse by estimating parameters that minimize the least squares error between measured and computed waveforms. Optimized parameters are compared across the control and the hypertensive groups to characterize vascular remodeling with disease. Results show that pulmonary hypertension is associated with stiffer and less compliant proximal and distal vasculature with augmented wave reflections, and that elastic nonlinearities are insignificant in the hypertensive animal. arXiv:1712.01699v2 [physics.flu-dyn] 17 May 2018 disease progression (Castelain et al., 2001;Hunter et al., 2011). In particular, the proximal arterial stiffness is an excellent predictor of mortality in patients with pulmonary arterial hypertension (Gan et al., 2007). Quantifying relative distributions of proximal and distal arterial stiffness (or compliance) and wave reflections in elevating the mPAP and PVR is vital for understanding disease mechanisms.In this study, we setup and calibrate a mathematical model predicting wave propagation in the pulmonary vasculature in C57BL6/J male mice with normal pulmonary function (control group (CTL), n = 7) and in mice with hypoxia-induced pulmonary hypertension (hypertensive group (HPH), n = 5) (Tabima et al., 2012;Vanderpool et al., 2011). The novelty of this study is the integration of high fidelity morphometric and hemodynamic data from multiple mice with a one dimensional (1D) model of large pulmonary arteries coupled with a zero dimensional (0D) model of the vascular beds. This is achieved by incorporating available data at each stage of the modeling including network extraction, parameter estimation and model validation. The outcome is used to infer disease progression by quantifying relative changes in PVR, proximal and distal arterial stiffness, compliance, and amplitudes of wave reflections, across the two groups (CTL and HPH)...