研究论文
A data-driven tracking control framework using physics-informed neural networks and deep reinforcement learning for dynamical systems
Ruan de Rezende Faria, Bruno Didier Olivier Capron, Argimiro R. Secchi, Maurício B. de Souza
Universidade Federal do Rio de Janeiro
来源Engineering Applications of Artificial Intelligence
年份2023
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学科主题
物理Model Reduction and Neural Networks
Advanced Control Systems Optimization · Iterative Learning Control Systems
参考文献 56
Applications of artificial neural networks in chemical engineering
被引 275D. M. Himmelblau · Korean Journal of Chemical Engineering · 2000
Reinforcement Learning: An Introduction
被引 25,758Sutton, Richard S., Barto, Andrew · IEEE Transactions on Neural Networks · 2005
Review of the applications of neural networks in chemical process control — simulation and online implementation
被引 384M.A. Hussain · Artificial Intelligence in Engineering · 1999
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引用本文 60
Physics-informed neural networks for PDE problems: a comprehensive review
被引 167Kuang Luo, J. Zhao, Ying‐Ping Wang · Artificial Intelligence Review · 2025
Transfer learning for improved generalizability in causal physics-informed neural networks for beam simulations
被引 52Taniya Kapoor, Hongrui Wang, Alfredo Núñez · Engineering Applications of Artificial Intelligence · 2024
A survey on physics informed reinforcement learning: Review and open problems
被引 47Chayan Banerjee, Kien Nguyen, Clinton Fookes · Expert Systems with Applications · 2025
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