Focus and Scope
JOURNAL AIMS & RESEARCH COVERAGE
Focus and Scope
Advancing mathematical modelling, rigorous analysis, optimization, and computational methods in biological and environmental systems.
Journal Focus
Bioenvironmental Modelling and Optimization (BEMO) is an international, peer-reviewed journal focusing on the development, mathematical analysis, numerical investigation, and optimization of mathematical models describing biological and environmental systems.
The journal emphasizes original contributions to mathematical modelling, particularly differential equation models, nonlinear dynamical systems, stability and bifurcation analysis, mathematical optimization, optimal control, and numerical and computational methods.
Scope
BEMO welcomes original research contributions in six interconnected areas of mathematical modelling, mathematical analysis, optimization, statistical inference, and scientific computation.
Mathematical Model Development
Development of deterministic, stochastic, and nonlinear mathematical models, including ordinary differential equations (ODEs), partial differential equations (PDEs), delay differential equations, and fractional-order models.
- Deterministic and stochastic mathematical models
- Ordinary and partial differential equation models
- Delay and functional differential equation models
- Fractional-order mathematical models
- Nonlinear and coupled mathematical systems
- Multiscale and spatially structured models
- Hybrid and data-informed mathematical models
- Mathematical model formulation, reduction, and theoretical justification
Mathematical Analysis of Dynamical Systems
Analysis of equilibrium points, stability, bifurcations, nonlinear dynamics, periodic solutions, and the long-term behaviour of dynamical systems.
- Existence, uniqueness, and well-posedness of solutions
- Equilibrium points and local stability analysis
- Global stability and Lyapunov methods
- Bifurcation theory and nonlinear dynamics
- Periodic solutions, oscillations, and limit cycles
- Persistence, extinction, and long-term dynamical behaviour
- Qualitative analysis of delay and fractional-order systems
- Threshold dynamics, multistability, and complex dynamical phenomena
Mathematical Optimization and Optimal Control
Optimization of mathematical models, optimal control, dynamic optimization, constrained optimization, and model-based intervention strategies.
- Optimal control of dynamical systems
- Pontryagin's maximum principle and necessary optimality conditions
- Dynamic programming and Hamilton-Jacobi-Bellman methods
- Constrained and nonlinear mathematical optimization
- Multiobjective and robust optimization
- Stochastic and uncertainty-aware optimization
- Parameter and intervention strategy optimization
- Numerical optimal control and computational optimization algorithms
Numerical and Computational Mathematical Modelling
Numerical solution of mathematical models, computational simulations, parameter estimation, sensitivity analysis, model calibration, and validation.
- Numerical methods for ordinary, partial, delay, and fractional differential equations
- Computational simulation and mathematical model implementation
- Parameter estimation and inverse problems
- Local and global sensitivity analysis and parameter identifiability
- Mathematical model calibration and validation
- Uncertainty quantification and propagation
- Numerical stability, convergence, consistency, and error analysis
- Computational bifurcation analysis and numerical continuation methods
Statistical Modelling, Inference, and Inverse Problems
Statistical and probabilistic approaches to mathematical modelling, parameter inference, inverse problems, uncertainty quantification, and the calibration of biological and environmental dynamical systems.
- Statistical and probabilistic modelling of dynamical systems
- Bayesian inference and probabilistic parameter estimation
- Inverse problems and mathematical model identification
- Structural and practical parameter identifiability
- Statistical model calibration and uncertainty quantification
- Likelihood-based inference and parameter uncertainty analysis
- Data assimilation and state estimation in dynamical systems
- Statistical validation and inference for biological and environmental models
Data-Driven and Scientific Machine Learning for Mathematical Modelling
Integration of data-driven methods and scientific machine learning with mathematical models, including physics-informed learning, differential equation discovery, surrogate modelling, and hybrid mechanistic-data-driven approaches for biological and environmental systems.
- Physics-informed neural networks (PINNs) for differential equation models
- Neural ordinary and partial differential equations
- Data-driven discovery of governing equations and dynamical systems
- Hybrid mechanistic and data-driven mathematical models
- Surrogate modelling and reduced-order model development
- Machine-learning-assisted parameter estimation and model calibration
- Scientific machine learning for inverse problems and system identification
- Data-driven prediction and optimization of biological and environmental dynamical systems
Application Domains
BEMO particularly encourages mathematically rigorous studies addressing biological and environmental systems. Applications may include cancer dynamics, epidemiology, population dynamics, ecological interactions, ecology, biodiversity conservation, environmental processes, and sustainable resource management. These application areas are illustrative rather than exhaustive, provided that the manuscript demonstrates a substantial mathematical contribution.
Manuscript Suitability
Manuscripts must demonstrate substantial contributions to mathematical model formulation, mathematical analysis, optimization, statistical inference for dynamical systems, or computational methodology. Data-driven and machine-learning approaches are within scope when they contribute meaningfully to the development, identification, analysis, or solution of mathematical models. Descriptive studies, routine numerical simulations, standalone statistical analyses, and generic machine-learning applications without meaningful mathematical modelling contributions are outside the journal's scope.
