Deadline Date: 31 October 2026
In the era of intelligent industrialization, engineering computing and communication systems are undergoing a fundamental paradigm shift, driven by the integration of advanced numerical techniques and AI—core themes aligned with RIMNI's focus on computational/numerical models, soft computing, and AI innovations. Traditional methods, long the backbone of engineering design and simulation (key journal research areas), face inherent bottlenecks in high-dimensional, nonlinear, real-time tasks due to high computational complexity, poor unstructured data adaptability, and inefficiency in handling inverse/ill-posed problems.
The rapid advancement of AI, machine learning, and soft computing provides transformative solutions, echoing RIMNI's emphasis on AI/ML and software innovation. Fusing AI's strengths in pattern recognition, adaptive learning, and data-driven modeling with numerical techniques' rigor enables more efficient, robust solutions for complex engineering tasks, advancing software/computer design and unlocking new applications, such as industrial IoT edge computing, AI-optimized communication protocols, digital twin-based maintenance.
The special issue welcomes submissions that fall within, but are not limited to, the following scopes:
Development and improvement of AI-enhanced numerical algorithms for engineering computing
AI-driven numerical optimization techniques for communication systems
Data-driven numerical modeling for digital twins in engineering
Advanced numerical methods for AI model training and deployment in edge/cloud engineering computing platforms
Stochastic numerical techniques combined with AI for uncertainty quantification in engineering and communication systems
Case studies and experimental validations of AI-numerical integrated solutions in real engineering/communication projects
Comparative analysis of traditional vs. AI-driven numerical techniques, evaluating their performance, efficiency, and scalability
Editorial board
