RIMNI Special Issue - Artificial Intelligence-Enhanced Numerical Methods for Science: Methods, Applications, and Challenges

Deadline Date: 30 June 2026

The convergence of Artificial Intelligence (AI) and classical numerical methods is creating a transformative paradigm in computational engineering and applied science. While traditional numerical techniques (e.g., FEM, FDM) provide rigorous frameworks for physical modeling, they often face challenges in computational cost and scalability for high-dimensional problems. This Special Issue, "Artificial Intelligence-Enhanced Numerical Methods for Science: Methods, Applications, and Challenges," aims to bridge the gap between data-driven intelligence and rigorous engineering calculation.

We invite contributions that demonstrate how AI can enhance, accelerate, or correct traditional numerical schemes rather than merely replace them. Topics of interest include, but are not limited to, physics-informed neural networks (PINNs), neural operators for partial differential equations, high-fidelity surrogate modeling for engineering design, and AI-driven acceleration of iterative solvers.

The scope emphasizes applications in solid mechanics, fluid dynamics, thermal engineering, and complex system optimization. Particular attention is given to methodologies that address the challenges of interpretability, error estimation, and generalization capability in AI-assisted numerical simulations. This issue seeks to advance reliable, efficient, and physically consistent AI tools specifically tailored for engineering calculation and design.

Deadline Date: 30 June 2026

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Prof. Dr. Wei Fang Email Website

Affiliation: Department of Computer Science, Nanjing University of Information Science & Technology, Nanjing, China

Research Interests: artificial intelligence for science, machine learning, big data in meteorology, computer vision

Dr. Yijing Li Email Website

Affiliation: Department of Geography, King's College London, Strand, London, United Kingdom

Research Interests: urban informatics, multimodal geospatial data mining, AI for urban science and sustainability

Dr. Amin Karami Email Website

Affiliation: Department of Engineering & Computing, University of East London, London, United Kingdom

Research Interests: big data technologies, computational intelligence, applied artificial intelligence

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Deadline Date: 30 June 2026

ISSN: ISSN 1886-158X (online version) - ISSN 0213-1315 (printed version)

Licence: Creative Commons BY-NC-SA license

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