Wearable devices offer continuous monitoring of biomarkers, presenting an opportunity to diagnose cardiovascular diseases earlier, potentially reducing their fatality rate. While machine learning holds promise for predicting cardiovascular biomarkers from sensor data, its use often depends on the availability of labeled datasets, which are limited due to technical and ethical constraints. On the other hand, biophysical simulations present a solution to data scarcity but face challenges in model transfer from simulation to reality due to inherent model simplifications and misspecifications. Building on advancements in hybrid learning, we introduce a method that combines a pulse-wave propagation model, rooted in biophysical simulations, with a correction model trained with unlabeled real-world data. This generative model transforms cardiovascular parameters into real-world sensor measurements and, when trained as an auto-encoder, also provides the inverse transformation, mapping measurements to cardiovascular biomarkers. Notably, when assessed using real pulse-wave data, our hybrid method appears to outperform models based solely on simulations in inferring cardiovascular biomarkers, opening new avenues for inferring physiological biomarkers in data-limited scenarios.