3 papers
q-bio.QM2026
Unveiling Scaling Laws of Parameter Identifiability and Uncertainty Quantification in Data-Driven Biological Modeling
Shun Wang, Wenrui Hao
Integrating high-dimensional biological data into data-driven mechanistic modeling requires rigorous practical identifiability to ensure interpretability and generalizability. Howe…
cs.LG2025
ZENN: A Thermodynamics-Inspired Computational Framework for Heterogeneous Data-Driven Modeling
Shun Wang, Shun-Li Shang, Zi-Kui Liu +1
Traditional entropy-based methods - such as cross-entropy loss in classification problems - have long been essential tools for representing the information uncertainty and physical…
q-bio.QM2025
A Systematic Computational Framework for Practical Identifiability Analysis in Mathematical Models Arising from Biology
Shun Wang, Wenrui Hao
Practical identifiability is a critical concern in data-driven modeling of mathematical systems. In this paper, we propose a novel framework for practical identifiability analysis…