Efficient data collection for establishing practical identifiability via active learning

Practical identifiability analysis (PIA) plays a crucial role in model development by assessing whether the available data are sufficient to yield reliable parameter estimates. In bioengineering applications, identifying the minimal experimental design that ensures parameter identifiability is essential in order to reduce cost, time, and resource consumption. In this paper, we introduce E-ALPIPE, a sequential active learning algorithm that recommends new data collection points most likely to establish practical identifiability given the current data, mathematical model and noise assumptions. We empirically evaluate E-ALPIPE against both a benchmark algorithm from the literature and random sampling over three synthetic experiments. Our results show that E-ALPIPE substantially reduces the number of observations required to achieve practical identifiability, while producing comparable or narrower confidence intervals and more accurate point estimates of system dynamics.