Deep Learning for Fast Inference of Mechanistic Models' Parameters
Maxim Borisyak Stefan Born Peter Neubauer Mariano Nicolas Cruz-Bournazou
Inferring parameters of macro-kinetic growth models, typically represented by
Ordinary Differential Equations (ODE), from the experimental data is a crucial
step in bioprocess engineering. Conventionally, estimates of the parameters are
obtained by fitting the mechanistic model to observations. Fitting, however,
requires a significant computational power. Specifically, during the
development of new bioprocesses that use previously unknown organisms or
strains, efficient, robust, and computationally cheap methods for parameter
estimation are of great value. In this work, we propose using Deep Neural
Networks (NN) for directly predicting parameters of mechanistic models given
observations. The approach requires spending computational resources for
training a NN, nonetheless, once trained, such a network can provide parameter
estimates orders of magnitude faster than conventional methods. We consider a
training procedure that combines Neural Networks and mechanistic models. We
demonstrate the performance of the proposed algorithms on data sampled from
several mechanistic models used in bioengineering describing a typical
industrial batch process and compare the proposed method, a typical
gradient-based fitting procedure, and the combination of the two. We find that,
while Neural Network estimates are slightly improved by further fitting, these
estimates are measurably better than the fitting procedure alone.