With the rapid development of the fields of new energy and petroleum engineering, it is difficult for traditional optimization algorithms to meet the needs of complex systems. The purpose of this research is to explore artificial intelligence large-model optimization algorithms for new energy and petroleum engineering. Convolutional neural networks are used to design and train optimization algorithm models. In the process of model training, we used a high-performance computing platform to accelerate the convergence and optimization process of the algorithm. The performance and effect of the proposed algorithm in the field of new energy and petroleum engineering are evaluated through experiments and comparative analysis. The experimental results show that the efficiency of the algorithm in this paper is between 89% and 98%, and the algorithm shows high optimization ability on large-scale data sets. Artificial intelligence large-model optimization algorithms for new energy and petroleum engineering have the potential to play an important role in this field, and provide a valuable reference for future research and application.

Lei Zhang. Research on Artificial Intelligence Large-model Optimization Algorithms for New Energy and Petroleum Engineering. International Journal of Frontiers in Engineering Technology (2023), Vol. 5, Issue 12: 8-13. https://doi.org/10.25236/IJFET.2023.051202.

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