IJMMNO proposes and fosters discussion on the mathematical modelling, algorithm development, nume... more IJMMNO proposes and fosters discussion on the mathematical modelling, algorithm development, numerical methods, computer simulations and numerical optimisation as well as applications and case studies. As a fully refereed international journal, IJMMNO will
* focus on the multidisciplinary and cross-disciplinary research
* communicate new algorithms and techniques in mathematical modelling and numerical optimisation
* promote real-world applications in all major areas of sciences, engineering and industry
IJMMNO publishes full-length original research papers, with a section dedicated to short research notes and concise case studies using modelling, simulation and optimisation.
IJMMNO aims to cover a wide range of research areas in applied mathematics, numerical algorithms, mathematical modelling and optimisation techniques and applications, including but not limited to
* Mathematical modelling and mathematical analysis
* Review, analysis and comparison of mathematical and numerical models
* New mathematical models and novel algorithms
* Numerical algorithm formulation and analysis (evolutionary, stochastic, nature-inspired and higher-level algorithms)
* Modelling of physical, chemical, biological, environmental and industrial processes
* Numerical analysis, error estimation, and stability
* Numerical methods, including solution of PDEs, finite difference methods, finite volume methods, finite element methods, meshless and element-free methods, extended finite element methods, discrete element methods, boundary element methods, and smooth particle hydrodynamics, spectral methods, and others
* Computer simulations and visualisation * Optimisation techniques (linear and nonlinear programming, stochastic search, nature-inspired algorithms and techniques such as particle swarm optimisation, simulated annealing, genetic algorithms, and other metaheuristic algorithms)
* Statistical simulations and techniques (Monte Carlo, Markov chain Monte Carlo, stochastic modelling and analysis, machine learning, Bayesian inference)
* Network modelling and theory (small-world networks, scale-free networks, power-law networks, financial networks, neural networks)
* Soft computing, natural computing, and bio-inspired computing with focus on algorithm development
* Review and comparison studies of algorithms and techniques (both conventional and unconventional)
* Multidisciplinary research combining algorithms, modelling and optimisation
* Case studies and applications in all areas of sciences, engineering and industry, including economics, earth sciences, environmental science, meteorology, and social sciences
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Papers by IJMMNO IJMMNO
* focus on the multidisciplinary and cross-disciplinary research
* communicate new algorithms and techniques in mathematical modelling and numerical optimisation
* promote real-world applications in all major areas of sciences, engineering and industry
IJMMNO publishes full-length original research papers, with a section dedicated to short research notes and concise case studies using modelling, simulation and optimisation.
IJMMNO aims to cover a wide range of research areas in applied mathematics, numerical algorithms, mathematical modelling and optimisation techniques and applications, including but not limited to
* Mathematical modelling and mathematical analysis
* Review, analysis and comparison of mathematical and numerical models
* New mathematical models and novel algorithms
* Numerical algorithm formulation and analysis (evolutionary, stochastic, nature-inspired and higher-level algorithms)
* Modelling of physical, chemical, biological, environmental and industrial processes
* Numerical analysis, error estimation, and stability
* Numerical methods, including solution of PDEs, finite difference methods, finite volume methods, finite element methods, meshless and element-free methods, extended finite element methods, discrete element methods, boundary element methods, and smooth particle hydrodynamics, spectral methods, and others
* Computer simulations and visualisation
* Optimisation techniques (linear and nonlinear programming, stochastic search, nature-inspired algorithms and techniques such as particle swarm optimisation, simulated annealing, genetic algorithms, and other metaheuristic algorithms)
* Statistical simulations and techniques (Monte Carlo, Markov chain Monte Carlo, stochastic modelling and analysis, machine learning, Bayesian inference)
* Network modelling and theory (small-world networks, scale-free networks, power-law networks, financial networks, neural networks)
* Soft computing, natural computing, and bio-inspired computing with focus on algorithm development
* Review and comparison studies of algorithms and techniques (both conventional and unconventional)
* Multidisciplinary research combining algorithms, modelling and optimisation
* Case studies and applications in all areas of sciences, engineering and industry, including economics, earth sciences, environmental science, meteorology, and social sciences