Neural Networks and Intelligent Optimization for Scientific Computing
A special issue of Computation (ISSN 2079-3197). This special issue belongs to the section "Computational Engineering".
Deadline for manuscript submissions: 30 April 2027 | Viewed by 127
Editor
Interests: neural networks; matrix theory; numerical linear algebra; applied mathematics; optimization methods; artificial intelligence; machine learning
Special Issue Information
Dear Colleagues,
The rapid evolution of artificial intelligence and optimization methodologies has created new paradigms for addressing challenging problems in scientific computing. In particular, neural-network-based computational frameworks and intelligent optimization algorithms have demonstrated remarkable potential in solving large-scale, nonlinear, and high-dimensional mathematical models arising in science, engineering, and data-driven applications. This Special Issue aims to bring together recent advances in the theory, design, analysis, and implementation of neural networks and intelligent optimization techniques for scientific computing.
Topics of interest include, but are not limited to, recurrent and dynamic neural networks, gradient-based neural computation, continuous-time and discrete-time optimization dynamics, intelligent and bio-inspired optimization algorithms, machine learning-assisted numerical methods, and hybrid computational intelligence frameworks. Particular attention is devoted to neural and optimization approaches for solving systems of linear and nonlinear equations, matrix equations, variational inequalities, constrained optimization problems, differential and partial differential equations, inverse problems, and large-scale computational models.
Contributions addressing rigorous theoretical aspects, such as stability analysis, convergence properties, robustness, computational complexity, Lyapunov-based methods, and numerical performance evaluation, are especially encouraged. The Special Issue also welcomes innovative applications in control systems, robotics, signal and image processing, scientific data analytics, computational physics, engineering design, and emerging interdisciplinary areas where intelligent computational methodologies can significantly enhance accuracy, efficiency, and scalability. By fostering the integration of neural computation, optimization theory, and advanced numerical techniques, this Special Issue seeks to highlight state-of-the-art developments and future research directions in scientific computing.
Dr. Dimitrios Gerontitis
Guest Editor
Manuscript Submission Information
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Keywords
- neural networks
- intelligent optimization
- scientific computing
- recurrent neural networks
- gradient-based neural computation
- computational intelligence
- matrix equations
- dynamical systems
- numerical optimization
- convergence analysis
- stability analysis
- differential equations
- machine learning
- control systems
- high-performance computing
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