  Driven by the critical needs of biomanufacturing 4.0, we introduce a
probabilistic knowledge graph hybrid model characterizing the risk- and
science-based understanding of bioprocess mechanisms. It can faithfully capture
the important properties, including nonlinear reactions, partially observed
state, and nonstationary dynamics. Given very limited real process
observations, we derive a posterior distribution quantifying model estimation
uncertainty. To avoid the evaluation of intractable likelihoods, Approximate
Bayesian Computation sampling with Sequential Monte Carlo (ABC-SMC) is utilized
to approximate the posterior distribution. Under high stochastic and model
uncertainties, it is computationally expensive to match output trajectories.
Therefore, we create a linear Gaussian dynamic Bayesian network (LG-DBN)
auxiliary likelihood-based ABC-SMC approach. Through matching the summary
statistics driven through LG-DBN likelihood that can capture critical
interactions and variations, the proposed algorithm can accelerate hybrid model
inference, support process monitoring, and facilitate mechanism learning and
robust control.
