High-Performance Computing-Driven AI Learning and Optimization

Dear Colleagues,

High-performance computing (HPC) is increasingly becoming a key driver of artificial intelligence (AI) learning and optimization. Large-scale deep learning, scientific machine learning, reinforcement learning, evolutionary computation, and intelligent optimization now face shared mathematical and computational challenges, including high-dimensional models, massive data, heterogeneous accelerators, multi-node communication, energy constraints, and resource competition. These challenges call for new mathematical modeling, parallel algorithms, scalable optimization theory, and reproducible experimental frameworks. This Special Issue focuses on HPC-driven AI learning and optimization, and welcomes original research and review articles on parallel and distributed learning; deep learning algorithms for GPUs and heterogeneous systems; scalable intelligent optimization; black-box optimization; reinforcement learning and evolutionary computation; large-scale hyperparameter and neural architecture optimization; optimization of complex engineering systems; modeling, simulation, and discovery in AI for Science; AI methods for engineering applications; AI-HPC system performance prediction; task scheduling; resource allocation; energy-aware optimization; surrogate modeling; and uncertainty-aware optimization. Contributions with clear mathematical problem formulations, algorithmic innovation, complexity or convergence analysis, and validation on large-scale or resource-constrained computing platforms are particularly encouraged.

Dr. Sichen Tao
Guest Editor

Manuscript Submission Information

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