Conditional LDDMM flow matching: generative shape modeling for domain uncertainty quantification
This talk gave a brief overview of ongoing research into the use of a novel generative model for domain shape uncertainty quantification based on conditional LDDMM flow matching, applied to pulsatile flow in the aorta.
Authors
- Sarah Katz (ORCID: https://orcid.org/0009-0001-5990-4704)
Institutions
- Weierstrass Institute for Applied Analysis and Stochastics (DE)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-03-03
- DOI
- https://doi.org/10.5281/zenodo.18861220
- Primary Topic
- Model Reduction and Neural Networks
- Type
- article
- Field-Weighted Citation Impact
- 0.00