# Deep Gaussian Process-based Multi-fidelity Bayesian Optimization for Simulated Chemical Reactors

arXiv: https://arxiv.org/abs/2210.17213 · HTML: https://mlsanity.com/paper/2210.17213/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 23 references that are in the map.
- First posted on arXiv 31 Oct 2022.

## Builds on (most cited first, 7 of 7)

- [Hyper-Parameter Optimization: A Review of Algorithms and Applications](https://mlsanity.com/paper/2003.05689/) (2020)
- [Practical Multi-fidelity Bayesian Optimization for Hyperparameter Tuning](https://mlsanity.com/paper/1903.04703/) (2019)
- [Deep Gaussian Processes for Multi-fidelity Modeling](https://mlsanity.com/paper/1903.07320/) (2019)
- [Multi-fidelity Gaussian Process Bandit Optimisation](https://mlsanity.com/paper/1603.06288/) (2016)
- [BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters](https://mlsanity.com/paper/1908.06756/) (2019)
- [Emulation of physical processes with Emukit](https://mlsanity.com/paper/2110.13293/) (2021)
- [Safe Real-Time Optimization using Multi-Fidelity Gaussian Processes](https://mlsanity.com/paper/2111.05589/) (2021)

## Built on by (most cited first, 2 of 2)

- [Machine Learning-Assisted Discovery of Flow Reactor Designs](https://mlsanity.com/paper/2308.08841/) (2023)
- [Multi-Fidelity Methods for Optimization: A Survey](https://mlsanity.com/paper/2402.09638/) (2024)

## Abstract (arXiv)

New manufacturing techniques such as 3D printing have recently enabled the creation of previously infeasible chemical reactor designs. Optimizing the geometry of the next generation of chemical reactors is important to understand the underlying physics and to ensure reactor feasibility in the real world. This optimization problem is computationally expensive, nonlinear, and derivative-free making it challenging to solve. In this work, we apply deep Gaussian processes (DGPs) to model multi-fidelity coiled-tube reactor simulations in a Bayesian optimization setting. By applying a multi-fidelity Bayesian optimization method, the search space of reactor geometries is explored through an amalgam of different fidelity simulations which are chosen based on prediction uncertainty and simulation cost, maximizing the use of computational budget. The use of DGPs provides an end-to-end model for five discrete mesh fidelities, enabling less computational effort to gain good solutions during optimization. The accuracy of simulations for these five fidelities is determined against experimental data obtained from a 3D printed reactor configuration, providing insights into appropriate hyper-parameters. We hope this work provides interesting insight into the practical use of DGP-based multi-fidelity Bayesian optimization for engineering discovery.