Integration of artificial intelligence and mathematical models for solving complex problems: a literature review
DOI:
https://doi.org/10.63688/95dv3370Keywords:
artificial intelligence, mathematical models, complex problem-solving, machine learning, computational optimizationAbstract
This study aimed to analyze the integration of artificial intelligence and mathematical models in solving complex problems across scientific and technological disciplines. A structured literature review with a qualitative and descriptive-analytical approach was conducted, considering 15 scientific publications from 2019 to 2023. The documentary analysis was organized into five thematic categories: integration of artificial intelligence and mathematical foundations, algorithmic optimization and computational simulation, prediction of dynamic systems and natural phenomena, applications in scientific discovery, and interpretability of intelligent models. The reviewed studies examined approaches such as physics-informed neural networks, neural operators, machine learning, deep reinforcement learning, numerical simulation, and computational prediction. The findings identified opportunities to approximate solutions to differential equations, optimize mathematical algorithms, improve computational simulations, predict dynamic phenomena, and support scientific discovery. However, differences in computational architectures, mathematical formulations, evaluation metrics, and application contexts prevented the establishment of consistent quantitative effects across the studies. It was concluded that integrating artificial intelligence with mathematical models provides valuable opportunities for solving complex problems when supported by rigorous mathematical formulation, reliable data, computational validation, and appropriate interpretation of results. Further research is recommended to compare intelligent algorithms with conventional mathematical methods and evaluate their accuracy, computational efficiency, generalizability, and interpretability.
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Copyright (c) 2026 Edwin Xavier Escobar Guanoluisa, Roberth Damián Guamani Guamani, Alex Fernando León Chancusig, Gladys Marisol Rodríguez Vallejo, María José Peñaranda Loor (Autor/a)
This work is licensed under a Creative Commons Attribution 4.0 International License.
