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A Practical Identifiability Criterion Leveraging Weak-Form Parameter Estimation
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AI
DOI：10.1007/s11538-026-01639-x.png)
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Abstract
En 中文
In this work, we define a practical identifiability criterion, (e, q)-identifiability, based on a parameter e, reflecting the noise in observed variables, and a parameter q, reflecting the mean-square error of the parameter estimator. This criterion is better able to encompass changes in the quality of the parameter estimate(s) due to increased noise in the data (compared to existing criteria based solely on average relative errors). We illustrate the usefulness of the criteria in several challenging identifiability studies, involving parameter estimation in partially observed systems. Furthermore, we leverage a weak-form equation error-based method of parameter estimation for systems with unobserved variables to assess practical identifiability far more quickly in comparison to output error-based parameter estimation. We do so by generating weak-form input-output equations using differential algebra techniques, as previously proposed by Boulier et al. (2014), and then applying Weak form Estimation of Nonlinear Dynamics (WENDy) to obtain parameter estimates. This method is computationally efficient and robust to noise, as demonstrated through two classical biological modeling examples.
Keywords:
Identifiability
Weak form
Data-driven modeling
Journal
B
IF:
2.2
Papers:
107
Citations:
5.7K
