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Global Optimization Employing Gaussian Process-Based Bayesian Surrogates.

Roland Preuss1, Udo Von Toussaint1

  • 1Max-Planck-Institute for Plasma Physics, EURATOM Association, 85748 Garching, Germany.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary
This summary is machine-generated.

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This study introduces a Bayesian global optimization method using Gaussian processes to efficiently find optimal parameters for complex physics simulations. The approach accelerates discovery by creating surrogate models and alternating utility functions to identify simulation maxima with reduced computational cost.

Area of Science:

  • Computational Physics
  • Applied Mathematics
  • Machine Learning

Background:

  • Complex physics simulations demand significant computational resources, necessitating efficient parameter optimization.
  • Global optimization techniques are crucial for identifying optimal input parameters from existing simulation data.
  • Bayesian frameworks offer a probabilistic approach to model complex systems.

Purpose of the Study:

  • To develop an efficient global optimization strategy for expensive physics simulations.
  • To leverage Gaussian processes for creating accurate surrogate models of complex functions.
  • To minimize computational cost while maximizing the discovery of optimal parameters.

Main Methods:

  • Utilized Gaussian processes within a Bayesian framework to build self-consistent surrogate models.
Keywords:
gaussian processglobal optimizationparametric studies

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  • Employed expectation values of hyperparameters for computational efficiency.
  • Alternated between expected improvement and maximum variance utility functions to guide parameter selection.
  • Iteratively updated the surrogate model with new data points until convergence.

    • Main Results:

    • Demonstrated proof of principle for the proposed global optimization method using mock data in one and two dimensions.
    • The surrogate model effectively guided the search towards the extremum of the underlying function.
    • Alternating utility functions mitigated the limitations of individual approaches.

    Conclusions:

    • The proposed Bayesian global optimization approach offers an efficient strategy for parameter exploration in computationally intensive simulations.
    • Gaussian process surrogate models, combined with adaptive utility functions, can significantly reduce the number of required simulations.
    • This method provides a viable solution for accelerating scientific discovery in fields reliant on complex modeling.