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This chapter explores the integration of machine learning and optimizationOptimization techniques in DEMDiscrete Element Method (DEM) simulations. It covers key machine learning strategies, research focuses on application and practical examples. The discussion extends toBayesian optimization Bayesian optimizationOptimization and genetic algorithmsGenetic Algorithms (GA), demonstrating their use in calibrating material parameters and enhancing computational efficiencyEfficiency to overcome traditional DEMDiscrete Element Method (DEM) limitations.

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Machine Learning and Optimization

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  • 发表日期：

    2026年

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  • Ramesh K. Agarwal,
  • Yali Shao

摘要

This chapter explores the integration of machine learning and optimizationOptimization techniques in DEMDiscrete Element Method (DEM) simulations. It covers key machine learning strategies, research focuses on application and practical examples. The discussion extends toBayesian optimization Bayesian optimizationOptimization and genetic algorithmsGenetic Algorithms (GA), demonstrating their use in calibrating material parameters and enhancing computational efficiencyEfficiency to overcome traditional DEMDiscrete Element Method (DEM) limitations.