crossrefNonlinear Analysis: Hybrid Systems2026-08-01Cited by 0
Learning a network digital twin as a hybrid system
Christos Mavridis, Fernando S. Barbosa, Hamed Farhadi, Karl H. Johansson
Christos Mavridis, Fernando S. Barbosa, Hamed Farhadi, Karl H. Johansson
Heesang Eom, Younghun Kim, Jongho Paik
This paper presents a digital twin-based river management and flood prediction system designed for hydrological environments, including volcanic geology. To address the problems of rapid runoff and complex terrain, a deep learning-based hybrid model is proposed that integrates a…
Amged Sayed, Samah Alshathri, Ezz El-Din Hemdan
In recent years, digital twin (DT) technology has garnered significant interest from both academia and industry. However, the development of effective fault detection and diagnosis models remains challenging due to the lack of comprehensive datasets. To address this issue, we pro…
High-fidelity models (HFMs) for twin-screw wet granulation (TSWG) are often too computationally expensiv//e for routine calibration, optimization, and digital twin deployment. This chapter presents a faster, cheaper, and easier-to-use surrogate modeling workflow that preserves HF…
Narges Mohaghegh, Hai Wang, Amirmehdi Yazdani
Reliable transfer of control policies from simulation to real-world robotic systems remains a central challenge in robotics, particularly for car-like mobile robots. Digital Twin (DT) technology provides a robust framework for high-fidelity replication of physical platforms and b…
Kalpana Eluri, Gayathri Krishnakumar, Karthikeyan Elumalai
Carbon nanotubes, graphene derivatives, MXenes, metal oxides, nanocellulose, and hybrid nanofillers exhibit outstanding reinforcement properties, such as mechanical strength, electrical conductivity, thermal transport, barrier properties, and multifunctionality, in thermoplastic…
Carlos Saldaña Enderica, José Ramon Llata, Carlos Torre-Ferrero
This study proposes a robust methodology for vibration suppression and trajectory tracking in rotary flexible-link systems by leveraging guided reinforcement learning (GRL). The approach integrates the twin delayed deep deterministic policy gradient (TD3) algorithm with a linear…