This paper was accepted at the workshop Machine Learning and the Physical Sciences at NeurIPS 2023.
Over the past decades, hemodynamics simulators have steadily evolved and have become tools of choice for studying cardiovascular systems in-silico. This comes naturally at the cost of increasing complexity since state-of-the-art models are non-linear partial differential equations depending on many parameters. While such tools are routinely used to simulate hemodynamics given physiological parameters, solving the related inverse problems — mapping waveforms to physiological parameters — has… This paper was accepted at the workshop Machine Learning and the Physical Sciences at NeurIPS 2023.
Over the past decades, hemodynamics simulators have steadily evolved and have become tools of choice for studying cardiovascular systems in-silico. This comes naturally at the cost of increasing complexity since state-of-the-art models are non-linear partial differential equations depending on many parameters. While such tools are routinely used to simulate hemodynamics given physiological parameters, solving the related inverse problems — mapping waveforms to physiological parameters — has… Read More