A closed-loop Neural ODE ROM with CNN-based feedback from growth features stabilizes long-horizon predictions of tissue growth and remodeling, reaching 90.3% clinical tolerance versus 43.7% for open-loop baselines.
Acta Biomaterialia137, 136–146 (2022)
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Stable Long-Horizon Neural ODE Reduced-Order Models via Learned Feedback for Biological Growth and Remodeling
A closed-loop Neural ODE ROM with CNN-based feedback from growth features stabilizes long-horizon predictions of tissue growth and remodeling, reaching 90.3% clinical tolerance versus 43.7% for open-loop baselines.