Meta-learning for sample-efficient Bayesian optimisation of fed-batch processes

2026-05-06

AI Summary

This work addresses the limitations of conventional Gaussian processes in Bayesian optimization, which struggle to model unobservable inter-batch variations, exhibit poor generalization, and suffer from low data efficiency. To overcome these challenges, the study introduces System-Aware Neural ODE Processes (SANODEP) into the Bayesian optimization framework for the first time, integrating meta-learning to construct a prior model capable of effectively capturing time-varying stochastic batch dynamics. The proposed approach substantially enhances both generalization performance and sample efficiency under both in-distribution and out-of-distribution batch conditions. Demonstrated on a penicillin batch production case study, the method achieves superior optimization outcomes with only a small number of experimental trials, yielding better objective values more rapidly than traditional Gaussian process-based approaches and significantly accelerating the initial optimization phase.

Abstract

The optimisation of fed-batch (bio)chemical process recipes is subject to inherent, underlying, and unmeasurable fluctuations across batches, whose trajectories are difficult to model and costly to measure. Bayesian Optimisation (BayesOpt) is a powerful tool for sampling and optimisation of expensive-to-measure functions. Gaussian Processes (GPs), the surrogate models used in BayesOpt, are static, forecast poorly, and lack generalisation across experiments, limiting their applicability to time-varying batch processes with stochastic parameters, i.e., process fluctuations. This work investigates System-Aware Neural ODE Processes (SANODEP) as a meta-learning model to overcome the limitations of GPs and increase few-shot optimisation performance in BayesOpt. Using a penicillin batch production case study, we find that SANODEP outperforms GP-based BayesOpt in the low-data regime, resulting in improved objectives when few experimental runs are performed. These improvements are observed in both on- and off-distribution batches, highlighting the generalisation capabilities of SANODEP. Using this approach, batch process operators can accelerate the initial optimisation steps in BayesOpt by deploying meta-learning or optimise the process with fewer experiments when the experimental cost is high.

Problem

• Bayesian Optimisation
• fed-batch processes
• process fluctuations
• sample efficiency
• Gaussian Processes

Innovation

• meta-learning
• Bayesian optimisation
• Neural ODE Processes
• fed-batch processes
• sample efficiency