University of Cambridge > > Isaac Newton Institute Seminar Series > Physics-Integrated Hybrid Modeling in Cardiac Digital Twins

Physics-Integrated Hybrid Modeling in Cardiac Digital Twins

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact nobody.

FHTW02 - Fickle Heart: The intersection of UQ, AI and Digital Twins

The interest in leveraging physics-based inductive bias in deep learning has resulted in recent developments of hybrid deep generative models (hybrid-DGMs) that integrates known physics-based mathematical expressions in neural generative models. The identification of these hybrid-DGMs often involves the inference of the parameters of the physics-based component along with its neural component. The identfiability of these hybrid-DGM however has not yet been theoretically probed or established. While the (un)identfiability of DGMs has been investigated,  existing solutions often do not apply here as they require observed auxiliary labels about the underlying hybrid models. In this talk, we probe the theoretical identfiability of hybrid-DGMs, present meta-learning as a novel solution to construct identifiable hybrid-DGMs, and examine empirical evidence for on synthetic and real-data benchmarks. We further discuss the possibility to extend the identification of these hybrid-DGMs to unsupervised settings using high-dimensional observation data (e.g., image sequences), and its proof-of-concept application to digital twins for cardiac electrophysiology.

This talk is part of the Isaac Newton Institute Seminar Series series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.


© 2006-2024, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity