Bridging Biological Modeling with Machine Learning for Dynamical Trajectory Prediction
Webinar
Biological systems exhibit complex, nonlinear dynamics that challenge traditional mechanistic modeling due to high computational cost and limited scalability. This talk explores AI surrogate models that integrate biological structure to enable scalable, uncertainty-aware prediction of biological dynamics, highlighted by an Alzheimer’s disease case study of disease progression and longitudinal clinical trajectories.