A Python surrogate modeling framework with derivatives

• A Data-Driven Method for the Derivation of Explicit Algebraic Reynolds Stress Models Applied to the Wake Losses of Low-Pressure Turbine Cascades

  2024 Journal of Turbomachinery article Engineering Turbomachinery Performance and Optimization

  0

  citations
• Understanding molecular abundances in star-forming regions using interpretable machine learning

  2023 Monthly Notices of the Royal Astronomical Society article Physics and Astronomy Astrophysics and Star Formation Studies Open access

  19

  citations
• A statistical and machine learning approach to the study of astrochemistry

  2023 Faraday Discussions article Physics and Astronomy Astrophysics and Star Formation Studies Open access

  5

  citations
• Multi-surrogate Assisted Efficient Global Optimization for Discrete Problems

  2022 IEEE Symposium Series on Computational Intelligence (SSCI) conference-paper Computer Science Advanced Multi-Objective Optimization Algorithms

  2

  citations
• Bayesian optimization using multiple directional objective functions allows the rapid inverse fitting of parameters for chromatography simulations

  2022 Journal of Chromatography A article Chemistry Analytical Chemistry and Chromatography Open access

  24

  citations
• Using Statistical Emulation and Knowledge of Grain-surface Diffusion for Bayesian Inference of Reaction Rate Parameters: An Application to a Glycine Network

  2022 The Astrophysical Journal article Physics and Astronomy Astrophysics and Star Formation Studies Open access

  6

  citations

6 results