  Advances in computational science offer a principled pipeline for predictive
modeling of cardiovascular flows and aspire to provide a valuable tool for
monitoring, diagnostics and surgical planning. Such models can be nowadays
deployed on large patient-specific topologies of systemic arterial networks and
return detailed predictions on flow patterns, wall shear stresses, and pulse
wave propagation. However, their success heavily relies on tedious
pre-processing and calibration procedures that typically induce a significant
computational cost, thus hampering their clinical applicability. In this work
we put forth a machine learning framework that enables the seamless synthesis
of non-invasive in-vivo measurement techniques and computational flow dynamics
models derived from first physical principles. We illustrate this new paradigm
by showing how one-dimensional models of pulsatile flow can be used to
constrain the output of deep neural networks such that their predictions
satisfy the conservation of mass and momentum principles. Once trained on noisy
and scattered clinical data of flow and wall displacement, these networks can
return physically consistent predictions for velocity, pressure and wall
displacement pulse wave propagation, all without the need to employ
conventional simulators. A simple post-processing of these outputs can also
provide a cheap and effective way for estimating Windkessel model parameters
that are required for the calibration of traditional computational models. The
effectiveness of the proposed techniques is demonstrated through a series of
prototype benchmarks, as well as a realistic clinical case involving in-vivo
measurements near the aorta/carotid bifurcation of a healthy human subject.
