Deadline Date: 31 October 2026
Industrial and applied mathematics is increasingly shaping modern engineering through advanced numerical methods, high‑fidelity modeling and simulation, and computational tools for design and decision‑making. In parallel, artificial intelligence is accelerating numerical computing via data‑driven solvers, physics‑informed learning, learned surrogates, and optimization‑enhanced algorithms.
This Special Issue aims to capture state‑of‑the‑art contributions that bridge rigorous numerical analysis with impactful engineering applications across modeling, control, inverse problems, and AI‑enabled computation.
Topics of Interest (Non‑Exhaustive):
Mathematical Modeling & Simulation
• Computational and numerical models of engineering and industrial problems.
• Multiphysics simulation, reduced‑order modeling, uncertainty quantification, and model validation.
• Advanced computational mechanics and material modeling.
Differential Equations, Dynamical Systems, and Fractional Calculus
• Numerical methods for ODEs/PDEs, including stiff and multiscale systems.
• Fractional and variable‑order models and numerical realizations (Caputo, Riemann–Liouville, Caputo‑Fabrizio, Atangana–Baleanu, tempered operators, etc.).
• Stability, convergence, and error analysis for classical and fractional numerical schemes.
Control Theory & Automation
• Numerical optimization methods for robust/nonlinear control design.
• Observers, estimation, and fault detection with computational validation.
• Control and monitoring of energy systems and industrial processes.
Artificial Intelligence for Numerical Methods
• Physics‑informed learning (PINNs) and hybrid numerical–ML solvers.
• Operator learning and surrogate models for fast simulation and design.
• AI‑accelerated inverse problems, parameter identification, and data assimilation.
Inverse Problems, Statistics, and Stochastic Methods
• Deterministic and stochastic inverse problems and regularization.
• Stochastic calculus and probabilistic numerical methods for engineering.
• Data‑driven uncertainty modeling and risk‑aware decision support.
Editorial board
