Backpropagation of neural network dynamical models applied to flow control

Déda, Tarcísio; Wolf, William; Dawson, Scott T. M.

doi:10.1007/s00162-023-00641-6

Cited by 5 publications

(1 citation statement)

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“…Those digital twins are employed to estimate the state of the system at any given location and time, acting as virtual sensors. An example of such implementation to estimate flow around obstacles, stabilise vortex shedding, and reduce drag force has been presented in the work of Fan et al [22] and Déda et al [23]. One way to define control strategies is through the implementation of reinforcement learning algorithms, which is a machine learning area able to iteratively improve a policy within a model-free framework [24].…”

Section: Of 16mentioning

confidence: 99%

Heat Conduction Control using Deep Q-Learning approach with Physics-Informed Neural Networks

Gonçalves,

de Sá Rodrigues

2024

Preprint

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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.

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Section: Of 16mentioning

confidence: 99%