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Towards Generative Design of Computationally Efficient Mathematical Models with Evolutionary Learning.

Anna V Kalyuzhnaya1, Nikolay O Nikitin1, Alexander Hvatov1

  • 1Nature Systems Simulation Lab, National Center for Cognitive Research, ITMO University, 49 Kronverksky Pr., 197101 St. Petersburg, Russia.

Entropy (Basel, Switzerland)
|December 30, 2020
PubMed
Summary
This summary is machine-generated.

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This study introduces a generative design approach for automated evolutionary learning of mathematical models. It offers a generalized formulation and parallelized methods for efficient model structure identification and co-design.

Area of Science:

  • Computational modeling and simulation
  • Machine learning and artificial intelligence
  • Mathematical modeling

Background:

  • Automated mathematical model creation is computationally intensive.
  • Current methods for model design and co-design lack generalized formulations.
  • Integrating performance feedback into the design loop is challenging.

Purpose of the Study:

  • To present a generative design approach for efficient, automated evolutionary learning of mathematical models.
  • To propose a generalized formulation for model design and co-design workflows.
  • To investigate the role of performance models in the automated design process.

Main Methods:

  • Developed a generalized formulation for the modeling workflow.
  • Implemented a parallelized evolutionary learning algorithm for model structure identification.
Keywords:
automated learningco-designevolutionary learninggenerative designgenetic programming

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  • Applied the approach to both equation-based and composite machine learning models.
  • Incorporated performance models into the design loop.

    • Main Results:

    • Demonstrated a computationally efficient method for generative design of mathematical models.
    • Successfully identified model structures using evolutionary learning for diverse model types.
    • Validated the proposed approach through a series of experiments with varying models and resources.

    Conclusions:

    • The generative design approach enables efficient automated learning of mathematical models.
    • The generalized formulation facilitates streamlined model design and co-design.
    • Performance model integration enhances the automated model development process.