3 papers
cs.LG2026
Latent PDE mapping for efficient physics-informed learning across geometries with limited data
Ingvild Askim Adde, Mary M. Maleckar, Gabriel Balaban
In this study, we introduce latent PDE mapping, a broadly applicable physics-informed learning technique designed to enable efficient geometric generalization with sparse training…
cs.CV2026
Balancing Fidelity, Utility, and Privacy in Synthetic Cardiac MRI Generation: A Comparative Study
Madhura Edirisooriya, Dasuni Kawya, Ishan Kumarasinghe +5
Deep learning in cardiac MRI (CMR) is fundamentally constrained by both data scarcity and privacy regulations. This study systematically benchmarks three generative architectures:…
q-bio.TO2025
Physics-Informed Symbolic Regression for Elasticity Modeling in Cardiac Digital Twins
Sophia Ohnemus, Kristin Fullerton, Leto L. Riebel +4
Cardiac digital twins hold great promise for personalized medicine, but they currently depend on complex constitutive models of tissue mechanics that are often over-parameterized f…