AUT Journal of Modeling and Simulation


The Amirkabir Journal of Modeling and Simulation (AJMS) is a Bi-quarterly journal which 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 scope of the journal covers,

  1. Dynamic modeling (Modeling theory and methodology) 1.1. Mathematical (Analytical) Modeling 1.2. Model-free Modeling 1.2.1. Experimental Modeling 1.2.2. Heuristic (Symbolic) Modeling 1.2.3. Intelligent Modeling 1.3. Computational Algorithms 1.4. Novel and unconventional linear system modelling techniques 1.5. Nonlinear and Complex Systems Including Large-scale systems 1.6. Computational electromagnetics 1.7. Computational electrodynamics 1.8. Computational fluid dynamics 1.9. Computational heat, mass, and momentum transfer 1.10. M&S technology of continuous systems 1.11. Discrete systems 1.12. Hybrid systems 1.13. Intelligent systems & Self-organized criticality control 1.14. Mechatronic system 1.15. Biomedical systems 1.16. Aeronautical systems 1.17. financial and other social system modeling 1.18. Cognitive Modeling 1.19. Mechatronic systems 1.20. Chaotic systems 1.21. Fractional order Systems
  2. Simulation [System simulation theory and methodology] 2.1. Complex applications of high level simulation languages 2.2. Multi-parameter Optimization in simulation 2.3. Verification, validation of theory and technology 2.4. Theory of Approximation 2.4.1. Finite element methods 2.4.2. Finite difference methods 2.4.3. Distributed Computing 2.4.4. High Performance Computing 2.4.5. Pervasive Computing 2.4.6. Grid Computing 2.4.7. Cloud Computing 2.4.8. Quantum Computing 2.4.9. Molecular computing 2.4.10. Intelligent Computing 2.4.11. Bioinformatics 2.4.12. Knowledge Discovery 2.5. Engineering Technology simulation or Process simulation
  3. System identification methodology 3.1. Theory of Identification 3.2. Novel and unconventional linear system identification methods 3.3. Nonlinear and Complex System Identifications 3.4. Approximation Theory and System Identification 3.5. Intelligent based Identification 3.6. Hybrid system Identification
  4. Control 4.1. Control theory and methodology 4.2. Robust Control 4.3. Adaptive Control 4.4. Adaptive Control of Multivariable Processes 4.5. Optimal Control 4.6. Hierarchical Control System 4.7. Networked Control System 4.8. Model predictive control (MPC) 4.9. Stochastic control 4.10. Classical Nonlinear System Control 4.11. Chaotic Systems 4.12. Singular control systems 4.13. Fractional Order Systems 4.14. Intelligent Based Control (application) 4.14.1. neural networks 4.14.2. fuzzy (logic) control 4.14.3. machine learning 4.14.4. evolutionary computation 4.14.5. genetic algorithms 4.14.6. Neuro-fuzzy control 4.14.7. Intelligent agents (Cognitive/Conscious control) 4.15. Iterative Learning Control 4.16. Approximation Theory And Control 4.17. Industrial process Control methods 4.17.1. Industrial control system 4.17.2. Automation of process control (Industrial control system) Fieldbus Artificial neural network (ANN) in automation Distributed Control System (DCS) supervisory control and data acquisition (SCADA) system Manufacturing execution system (MES) 4.17.3. Model predictive control (MPC) for industrial control 4.17.4. Human machine interface (HMI) 4.17.5. Building automation system (BAS or BMS) 4.17.6. Instrumentation 4.17.7. Inferential control 4.17.8. Sequential control 4.17.9. SIS: Safety instrumented system
    4.17.10. MPO: Manufacturing planning optimization 4.18. Neuro - Controllers 4.19. Fuzzy Controllers 4.20. Fault tolerant Systems 4.20.1. Fault Analysis 4.20.2. Fault Detection 4.20.3. Fault Isolation 4.21. Theory of Complex Systems 4.21.1. Large scale control systems 4.21.2. Decentralized Control Systems (DCS) 4.21.3. Distributed Control Systems 4.21.4. Multi agent Control systems 4.21.5. Chaotic Systems 4.21.6. Network Control System (NCF) 4.22. Theory of Intelligence 4.22.1. Neuro-systems 4.22.2. Fuzzy Systems 4.22.3. Hybrid Systems 4.22.4. Self-organized criticality control 4.23. Applications of Control 4.23.1. Robotics- Manipulator 4.23.2. Robotics- Mobile robots 4.23.3. Mechatronic systems 4.23.4. Biological Systems 4.23.5. Aerospace- Navigation 4.23.6. Aerospace- Conductance 4.23.7. Complex and large scale systems 4.23.8. Hybrid Systems 4.23.9. Complementary medical system 4.23.10. Automated highway systems 4.23.11. intelligent transport systems (ITS) 4.23.12. Stochastic control (In finance) 4.23.13. Discrete event systems 4.23.14. Chaotic systems 4.23.15. MEMS
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