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

• URL: http://arxiv.org/abs/2605.05382v1
• Date: Wed, 06 May 2026 19:07:29 GMT
• Title: Meta-learning for sample-efficient Bayesian optimisation of fed-batch processes
• Abstract summary: optimisation of fed-batch (bio)chemical process recipes is subject to inherent, underlying, and unmeasurable fluctuations.<n>BayesOpt is a powerful tool for sampling and optimisation of expensive-to-measure functions.
• Score: 14.388871922519654
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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