ArticlePhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2025
InVAErt networks for amortized inference and identifiability analysis of lumped-parameter haemodynamic models.
Article in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- On the accuracy of implicit neural representations for cardiovascular anatomies and hemodynamic fields.Computers in biology and medicine · 2026Article
- Physics-Informed Emulation of Systemic Circulation for Fast Parameter Estimation and Uncertainty Quantification.International journal for numerical methods in biomedical engineering · 2026Article
- Patient-specific hemodynamic simulation for left ventricular assist device via non-invasive monitoring based on lumped parameter model and hierarchical neural network.Frontiers in physiology · 2026Article
- Preface to the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 2)'.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
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Authors and funding
3 authors.
Funding
Abstract
Estimation of cardiovascular model parameters from electronic health records (EHRs) poses a significant challenge primarily due to lack of identifiability. Structural non-identifiability arises when a manifold in the space of parameters is mapped to a common output, while practical non-identifiability can result due to limited data, model misspecification or noise corruption. To address the resulting ill-posed inverse problem, optimization-based or Bayesian inference approaches typically use regularization, thereby limiting the possibility of discovering multiple solutions. In this study, we use inVAErt networks, a neural network-based, data-driven framework for enhanced digital twin analysis of stiff dynamical systems. We demonstrate the flexibility and effectiveness of inVAErt networks in the context of physiological inversion of a six-compartment lumped-parameter haemodynamic model from synthetic data to real data with missing components.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 2)'.
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Registered trials
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