Aims and Scope

Most research and experiments in the fields of science and engineering have been spending significant efforts to extract rules from various complicated phenomena by observations, recorded data, analytical or experimental derivations and so on Modeling. The rules are normally formulated by quantitative expressions (quantitative models) or qualitative expressions (qualitative models). Simulation and Identification provides mechanisms to establish the Models and Control provides mechanisms to improve the performance of the system, represented by their models.

The AUT Journal of Modeling and Simulation (AUT J Model Simul), established in 2009, is a double-blind peer-reviewed Semiannual journal, owned, managed & published by Amirkabir University of Technology. The journal provides an international forum for researchers, scholars, and engineers in the fields of system modeling, identification, simulation and control to publish high-quality and refereed papers, including the latest theoretical results and their practical applications. The journal scope includes but are not restricted to, the following areas:

• Mathematical modeling
• Model-free (experimental) modeling
• Applications of modeling and simulation in the following disciplines:
  • Robotic and mechatronic systems
  • Biological and medical systems 
  • Electromagnetic systems
  • Agricultural and environmental systems
  • Industrial, military, aerospace systems
  • Power systems
  • Economic and financial systems
  • Social systems  
  • Civil and environmental systems
  • Mechanical and aerospace systems
  • Computer and information technology engineering
• System identification
• Linear and nonlinear control 
• Optimization and optimal control
• Robust control
• Adaptive control
• Networked and cooperative control 
• Distributed control
• Quantum control 
• Soft computing and control
• Iterative learning control 
• Data processing 
• Process control and instrumentation
• Control in power electronics
• Fault detection and isolation
• Model predictive control
• Stochastic control and filtering
• Vibration and noise control
• Neural networks and fuzzy logic
• Intelligent systems 
• Hybrid systems
• Discrete event systems 
• Multi-agent systems
• Communication systems