Speaker
Wenrui Hao
(Department of Mathematics, Pennsylvania State University, University Park, PA, USA)
Description
Integrating high-dimensional biological data into mechanistic models requires practical identifiability for reliable inference. We develop a computational framework that reveals scaling laws for identifiability via asymptotic analysis, combining Fisher information with perturbed Hessians to quantify identifiability and uncertainty in non-identifiable subspaces. Validated on several benchmark data-driven models, the framework uncovers key mechanisms and provides principled scaling laws for data-driven mechanistic modeling and digital twins.
Author
Wenrui Hao
(Department of Mathematics, Pennsylvania State University, University Park, PA, USA)
