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  3. Python-based Model Emulation Workflows With Pest.
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  3. Python-based Model Emulation Workflows With Pest.

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Python-Based Model Emulation Workflows with PEST.

Rui Hugman, Jeremy White1

  • 1INTERA Incorporated, Fort Collins, CO.

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View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces the Emulator module in pyEMU, offering a standardized tool for environmental model emulation. It bridges the implementation gap, enabling easier integration of surrogate models like Gaussian Process Regression (GPR) into existing workflows.

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Area of Science:

  • Environmental modeling
  • Computational science
  • Geosciences

Background:

  • High-fidelity environmental models face computational challenges for uncertainty quantification and optimization.
  • A lack of standardized tools hinders the integration of surrogate modeling (emulation) into existing workflows, creating an implementation gap.

Purpose of the Study:

  • To bridge the implementation gap by providing a standardized, plug-and-play framework for model emulation.
  • To facilitate the integration of emulation techniques into existing environmental modeling workflows.
  • To enable direct comparisons between physics-based models and emulators.

Main Methods:

  • Developed the Emulator module within the open-source pyEMU package.
  • Implemented a plug-and-play architecture for deploying Gaussian Process Regression (GPR) and Data-Space Inversion (DSI).
  • Automated workflow components, including non-Gaussian data transformation and PEST interface file generation.

    • Main Results:

    • The Emulator module provides a standardized framework for deploying various model emulation techniques.
    • Trained surrogates can serve as direct replacements for physics-based models, simplifying workflow integration.
    • Demonstrated utility through a benchmarking optimization problem and a synthetic groundwater model history-matching application.

    Conclusions:

    • The Emulator module in pyEMU effectively bridges the implementation gap for environmental model emulation.
    • Standardized tools like Emulator facilitate the adoption and comparison of emulation techniques.
    • This facilitates knowledge building within the community on the effective deployment of surrogate models.