Deep Gaussian Process-based Multi-fidelity Bayesian Optimization for
Simulated Chemical Reactors
- URL: http://arxiv.org/abs/2210.17213v1
- Date: Mon, 31 Oct 2022 10:52:16 GMT
- Title: Deep Gaussian Process-based Multi-fidelity Bayesian Optimization for
Simulated Chemical Reactors
- Authors: Tom Savage, Nausheen Basha, Omar Matar Ehecatl, Antonio Del-Rio
Chanona
- Abstract summary: We apply deep Gaussian processes (DGPs) to model multi-fidelity coiled-tube reactor simulations.
The search space of reactor geometries is explored through an amalgam of different fidelity simulations.
The accuracy of simulations is determined against experimental data obtained from a 3D printed reactor configuration.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: 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.
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