Meta-learning for sample-efficient Bayesian optimisation of fed-batch processes
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 limitation...
| Authors | Becky Langdon, Gabriel D. Patrón, Chrysoula D. Kappatou et al. |
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| Primary category | math.OC |
| Published | 6 May 2026 |
| Last updated at source | 6 May 2026 |
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