# Multi-fidelity Gaussian Process for Biomanufacturing Process Modeling with Small Data

arXiv: https://arxiv.org/abs/2211.14493 · HTML: https://mlsanity.com/paper/2211.14493/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 52 references that are in the map.
- First posted on arXiv 26 Nov 2022.

## Builds on (most cited first, 5 of 5)

- [Survey of multifidelity methods in uncertainty propagation, inference, and optimization](https://mlsanity.com/paper/1806.10761/) (2018)
- [Deep Gaussian Processes for Multi-fidelity Modeling](https://mlsanity.com/paper/1903.07320/) (2019)
- [Reinforcement Learning for Batch Bioprocess Optimization](https://mlsanity.com/paper/1904.07292/) (2019)
- [Knowledge transfer across cell lines using Hybrid Gaussian Process models with entity embedding vectors](https://mlsanity.com/paper/2011.13863/) (2020)
- [Safe model-based design of experiments using Gaussian processes](https://mlsanity.com/paper/2011.10009/) (2020)

## Built on by (most cited first, 1 of 1)

- [Transfer Learning Bayesian Optimization to Design Competitor DNA Molecules for Use in Diagnostic Assays](https://mlsanity.com/paper/2402.17704/) (2024)

## Abstract (arXiv)

In biomanufacturing, developing an accurate model to simulate the complex dynamics of bioprocesses is an important yet challenging task. This is partially due to the uncertainty associated with bioprocesses, high data acquisition cost, and lack of data availability to learn complex relations in bioprocesses. To deal with these challenges, we propose to use a statistical machine learning approach, multi-fidelity Gaussian process, for process modelling in biomanufacturing. Gaussian process regression is a well-established technique based on probability theory which can naturally consider uncertainty in a dataset via Gaussian noise, and multi-fidelity techniques can make use of multiple sources of information with different levels of fidelity, thus suitable for bioprocess modeling with small data. We apply the multi-fidelity Gaussian process to solve two significant problems in biomanufacturing, bioreactor scale-up and knowledge transfer across cell lines, and demonstrate its efficacy on real-world datasets.