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20242026
most citedAddressing Misspecification in Simulation-based Inference through Data-driven Calibration

3 citations · 4 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CE2026

A Bifurcation-Based Domain Decomposition Method with Neural Operators for Blood Flow Simulation

Yuzhou Zhao, Han Zhang, J. Matias Di Martino +2

Fast and accurate simulation of hemodynamic behavior within vascular networks is essential for numerous clinical applications. However, obtaining high-quality and computationally e…

cs.LG2025

Hybrid Modeling of Photoplethysmography for Non-invasive Monitoring of Cardiovascular Parameters

Emanuele Palumbo, Sorawit Saengkyongam, Maria R. Cervera +5

Continuous cardiovascular monitoring can play a key role in precision health. However, some fundamental cardiac biomarkers of interest, including stroke volume and cardiac output,…

cs.LG2025★ 1 cited

Inferring Optical Tissue Properties from Photoplethysmography using Hybrid Amortized Inference

Jens Behrmann, Maria R. Cervera, Antoine Wehenkel +8

Smart wearables enable continuous tracking of established biomarkers such as heart rate, heart rate variability, and blood oxygen saturation via photoplethysmography (PPG). Beyond…

cs.LG2024

Leveraging Cardiovascular Simulations for In-Vivo Prediction of Cardiac Biomarkers

Laura Manduchi, Antoine Wehenkel, Jens Behrmann +6

Whole-body hemodynamics simulators, which model blood flow and pressure waveforms as functions of physiological parameters, are now essential tools for studying cardiovascular syst…

stat.ML2024★ 3 cited

Addressing Misspecification in Simulation-based Inference through Data-driven Calibration

Antoine Wehenkel, Juan L. Gamella, Ozan Sener +4

Driven by steady progress in deep generative modeling, simulation-based inference (SBI) has emerged as the workhorse for inferring the parameters of stochastic simulators. However,…