Version 1
: Received: 13 June 2024 / Approved: 14 June 2024 / Online: 14 June 2024 (08:39:45 CEST)
How to cite:
Gonçalves, N.; de Sá Rodrigues, J. Heat Conduction Control using Deep Q-Learning approach with Physics-Informed Neural Networks. Preprints2024, 2024060978. https://doi.org/10.20944/preprints202406.0978.v1
Gonçalves, N.; de Sá Rodrigues, J. Heat Conduction Control using Deep Q-Learning approach with Physics-Informed Neural Networks. Preprints 2024, 2024060978. https://doi.org/10.20944/preprints202406.0978.v1
Gonçalves, N.; de Sá Rodrigues, J. Heat Conduction Control using Deep Q-Learning approach with Physics-Informed Neural Networks. Preprints2024, 2024060978. https://doi.org/10.20944/preprints202406.0978.v1
APA Style
Gonçalves, N., & de Sá Rodrigues, J. (2024). Heat Conduction Control using Deep Q-Learning approach with Physics-Informed Neural Networks. Preprints. https://doi.org/10.20944/preprints202406.0978.v1
Chicago/Turabian Style
Gonçalves, N. and Jhonny de Sá Rodrigues. 2024 "Heat Conduction Control using Deep Q-Learning approach with Physics-Informed Neural Networks" Preprints. https://doi.org/10.20944/preprints202406.0978.v1
Abstract
As modern systems are becoming more complex their control strategy cannot longer relay only on measurement information that usually comes from probes but also from mathematical models. Those systems models can lead to unbearable computation times due to their own complexity, turning the control process non-viable, which leads to the implementation of surrogate models that enable to achieve estimates within acceptable time to take decisions. A control trained with Deep Reinforcement Learning algorithm, using a Physics-Informed Neural Network to obtain the temperature map on the following time step, replaces the need of running Direct Numerical Simulations. On this work we considered an 1D heat conduction problem, which temperature distribution feeds a control system to activate a heat source aiming to obtain a constant, previously defined, temperature value. With this approach, control training becomes much faster without the need of performing numerical simulations or laboratory measurements, as well the control is taken based on Neural Network enabling its implementation on simple processors to edge computing.
Keywords
Physics-Informed Neural Network; Deep Q-Learning; Model Predictive Control; Heat Transfer
Subject
Engineering, Control and Systems Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
