Simulator-based surrogate optimisation employing adaptive uncertainty-aware sampling

Thumbnail Image

Files

Simulator-based_surrogate_optimisation_employing_adaptive_uncertainty-aware_sampling.pdf (2.16 MB)

Access rights

openAccess
CC BY
publishedVersion

URL

Journal Title

Journal ISSN

Volume Title

A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Unless otherwise stated, all rights belong to the author. You may download, display and print this publication for Your own personal use. Commercial use is prohibited.

Authors

Date

2025-10

Department

Department of Mathematics and Systems Analysis

Major/Subject

Mcode

Degree programme

Language

en

Pages

13

Series

Computers & Chemical Engineering, Volume 201, pp. 1-13

Abstract

Optimisation problems involving computationally expensive, black-box functions derived from high-fidelity engineering simulations remain challenging. To efficiently bridge the simulators and optimisation processes, we introduce an adaptive framework for surrogate modelling and optimisation. Our method employs low-discrepancy sequence sampling to select points, followed by training a surrogate model using a piecewise linear neural network (NN) with rectified linear unit (ReLU) activation. Using mixed-integer programming (MIP), we reformulate the ReLU NN as embedded components of an optimisation problem and solve it to find an optimal simulator input. This is achieved by iteratively refining the solution via resampling the simulator, retraining the surrogate model, and rebuilding and resolving the MIP problem. For resampling, an infill strategy that incorporates uncertainty assessment and a solution pool is employed, balancing exploration and exploitation. Moreover, computational efficiency is boosted by bound tightening, lossless model compression, and memory structure reuse. Validation on practical engineering applications confirms significant optimisation efficiency gains from the domain-refined strategy.

Description

Publisher Copyright: © 2025 The Authors

Keywords

Mathematical optimisation, Mixed-integer programming, Neural networks, Simulation

Other note

DOI

10.1016/j.compchemeng.2025.109243

Citation

Liu, Y & Oliveira, F 2025, 'Simulator-based surrogate optimisation employing adaptive uncertainty-aware sampling', Computers & Chemical Engineering, vol. 201, 109243, pp. 1-13. https://doi.org/10.1016/j.compchemeng.2025.109243

Permanent link to this item

https://urn.fi/URN:NBN:fi:aalto-202508045669

Collections

[article-cris] Perustieteiden korkeakoulu / SCI