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Sensor-Enhanced Machine Learning for Complex System Optimization

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: 20 December 2026 | Viewed by 937

Special Issue Editor


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Guest Editor
School of Innovation, Design and Engineering, Mälardalen University, 721 23 Västerås, Sweden
Interests: internet of things; computer networks; sensor networks; wireless communication; edge computing
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Special Issue Information

Dear Colleagues,

The rapid expansion of intelligent sensing technologies and machine learning (ML) is revolutionizing the design and optimization of complex systems across industries. As real-world systems grow in complexity, such as autonomous vehicles, energy infrastructures, smart factories, and medical devices, integrating real-time sensor data with machine learning (ML) models becomes essential for robust performance, adaptive control, and predictive maintenance.

This Special Issue aims to showcase the latest advances in sensor-enhanced machine learning (ML) techniques applied to the optimization of complex systems. We welcome original research, review articles, and case studies that explore novel algorithms, architectures, and applications at the intersection of sensor networks, edge computing, machine learning, and system control/optimization. The issue encourages interdisciplinary contributions bridging telecommunications, mechanical engineering, artificial intelligence, and cyber–physical systems.

Dr. Hossein Fotouhi
Guest Editor

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Keywords

  • complex system
  • optimization system
  • control/optimization
  • machine learning
  • reinforcement learning
  • cyber–physical systems
  • Internet of Things
  • digital twin
  • smart sensors
  • sensor networks
  • edge computing

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Published Papers (1 paper)

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Research

37 pages, 8061 KB  
Article
Sensor-Driven Surrogate Modeling and Control of Nonlinear Dynamical Systems Using FAE-CAE-LSTM and Deep Reinforcement Learning
by Mahdi Kherad, Mohammad Kazem Moayyedi, Faranak Fotouhi-Ghazvini, Maryam Vahabi and Hossein Fotouhi
Sensors 2025, 25(16), 5149; https://doi.org/10.3390/s25165149 - 19 Aug 2025
Cited by 1 | Viewed by 645
Abstract
In cyber-physical systems governed by nonlinear partial differential equations (PDEs), real-time control is often limited by sparse sensor data and high-dimensional system dynamics. Deep reinforcement learning (DRL) has shown promise for controlling such systems, but training DRL agents directly on full-order simulations is [...] Read more.
In cyber-physical systems governed by nonlinear partial differential equations (PDEs), real-time control is often limited by sparse sensor data and high-dimensional system dynamics. Deep reinforcement learning (DRL) has shown promise for controlling such systems, but training DRL agents directly on full-order simulations is computationally intensive. This paper presents a sensor-driven, non-intrusive reduced-order modeling (NIROM) framework called FAE-CAE-LSTM, which combines convolutional and fully connected autoencoders with a long short-term memory (LSTM) network. The model compresses high-dimensional states into a latent space and captures their temporal evolution. A DRL agent is trained entirely in this reduced space, interacting with the surrogate built from sensor-like spatiotemporal measurements, such as pressure and velocity fields. A CNN-MLP reward estimator provides data-driven feedback without requiring access to governing equations. The method is tested on benchmark systems including Burgers’ equation, the Kuramoto–Sivashinsky equation, and flow past a circular cylinder; accuracy is further validated on flow past a square cylinder. Experimental results show that the proposed approach achieves accurate reconstruction, robust control, and significant computational speedup over traditional simulation-based training. These findings confirm the effectiveness of the FAE-CAE-LSTM surrogate in enabling real-time, sensor-informed, scalable DRL-based control of nonlinear dynamical systems. Full article
(This article belongs to the Special Issue Sensor-Enhanced Machine Learning for Complex System Optimization)
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