Dear Colleagues,

The emergence of the Digital Twin paradigm for built assets has been possible, primarily, due to recent technological advances on sensors, the Internet of Things (IoT), and cloud computing. These technologies enable real-time data collection, communication, and computation, required by digital twins. However, beyond real-time monitoring of built assets, few advancements have been reported in the literature on simulation, optimisation, and automated operations approaches concerning built assets. Ideally, a Digital Twin will leverage real-time sensor data to simulate operations and change control parameters accordingly.

The purpose of this Special Issue is to contribute innovative research to the built environment field by showcasing simulation, optimisation, and automated operations approaches that leverage the Digital Twin paradigm across a wide range of built environment use-cases. Our objective is to compile an outstanding collection of research papers in the field of simulation, optimisation, and approaches for automated operations driven by Digital Twins in the built environment.

We encourage authors to consider this Special Issue as an opportunity to go beyond traditional disciplinary boundaries in computer science and the built environment; and to engage more broadly with varied approaches to simulation and automated operations. Researchers are invited to share their original research (theoretical and experimental), case studies, and comprehensive review papers addressing (but not limited to) the following subjects:

• Simulation approaches for Digital Twins
• Digital Twin approaches to optimal operations
• Digital Twin approaches to optimised designs
• Digital Twin approaches to automated operations
• Simulations that use real-time sensor data as inputs to adjust control parameters
• Validation approaches for Digital Twin simulations
• Calibration approaches for Digital Twin models
• Case studies addressing DT simulation, optimisation, and automated operations for any phase of built and infrastructure assets lifecycle.

Dr. Manuel Davila Delgado
Dr. Tom Beach
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• digital twin
• simulations
• optimisation
• model validation
• optimal operations
• automated operations
• data-driven modelling
• real-time modelling
• human–machine interaction
• autonomous agents
• multi-agent systems
• Markov processes
• behavioural cloning
• inverse reinforcement learning
• imitation learning
• Q learning
• deep reinforcement learning
• reinforcement learning