Symbolic Regression Based Surrogate Modelling of a High-Fidelity Multiphysics CO₂ Corrosion Model

Woollam, R.C., Proudlove, E., Jones, M. et al. (2 more authors) (2025) Symbolic Regression Based Surrogate Modelling of a High-Fidelity Multiphysics CO₂ Corrosion Model. In: Proceedings of the CONFERENCE 2025. AMPP Annual Conference + Expo 2025, 06-10 Apr 2025, Nashville, Tennessee. . Association for Materials Protection and Performance (AMPP). Article no: C2025-00433.

Metadata

Item Type: Proceedings Paper
Authors/Creators:
Keywords: Symbolic Regression, Deep Neural Network, Machine Learning, Carbon Dioxide Corrosion, Modeling, Simulation
Dates:
  • Published (online): 6 April 2025
  • Published: 6 April 2025
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mechanical Engineering (Leeds)
Date Deposited: 19 May 2026 12:34
Last Modified: 19 May 2026 12:34
Published Version: https://content.ampp.org/ampp/proceedings-abstract...
Status: Published
Publisher: Association for Materials Protection and Performance (AMPP)
Identification Number: 10.5006/c2025-00433
Open Archives Initiative ID (OAI ID):

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