Articles

High-Performance Computing (HPC) is indispensable for large-scale scientific simulations, but achieving optimal performance on modern supercomputers is increasingly challenging. As HPC systems scale toward exascale, they face escalating complexity in hardware, software, and workloads. Traditional optimization methods (manual tuning and heuristic algorithms) struggle to cope with dynamic workloads and intricate system behaviors. Artificial Intelligence (AI) techniques offer a promising approach to address these challenges. This article provides an overview of AI-powered optimization in HPC, focusing on how machine learning and related AI methods enhance performance, efficiency, and scalability of scientific simulations. We survey key AI techniques applied to HPC optimization including machine learning for performance modeling, deep reinforcement learning for resource management, and AI-driven surrogate models for accelerating simulations and illustrate their impact through case studies in domains such as job scheduling and fluid dynamics. We discuss the practical applications of these techniques, highlighting reported performance gains (e.g., substantial reductions in simulation run time and improved resource utilization). We also examine the challenges in integrating AI with HPC (such as training overhead, data movement, and reliability concerns) and outline future directions for research. The convergence of AI and HPC is poised to produce “smart” simulation workflows that intelligently adapt and optimize in real time, pushing the frontiers of scientific computing.

This paper is a continuation of the researches which were carried out by [1,2,3]. Accordingly, this manuscript proposes analytical analysis of a novel triple skin Concrete Filled Steel Tube (CFST) under the effect of sudden impact. Moreover, this is done by extending the double skinned CFST design and installing a third inner CFST inside the second inner tube to achieve the proposed triple skinned CFST design. Furthermore, the propositions consist of two parts. Where the first proposition is a novel triple skin CFST design under the effect of sudden impact, with first sandwich layer filled with Ultra High-Performance Fiber Reinforced Concrete (UHPFRC) and second sandwich layer filled with Normal Strength Concrete (NSC). While the second proposition is a novel triple skin CFST under the effect of sudden impact, with first sandwich layer filled with UHPFRC, second sandwich layer filled with NSC and third skin internal tube filled with NSC. It is strongly believed by the author of this manuscript that (1) the first proposition of novel triple skin CFST will increase the impact resistivity of the structural member by 25 to 32% and (2) it is predicted that the second proposition of novel triple skin CFST will boost the efficiency of the structural member under the even of sudden impact by 28 to 36%.

Architectural frameworks for large-scale Electronic Health Record (EHR) data platforms are described. Existing EHR data platform architectures often leverage multiple cloud-based solutions blended with institutional infrastructures to manage and analyze clinical data at scale. Key design principles governing the scale of existing EHR data architecture include model design, governance structure, data access management, data security/policy/protection, data-information-language-based standardization, and analytics tool alignment, among others. The rapidly evolving technology landscape and the unprecedented volume of incident and retrospective clinical data being collected and generated within healthcare organizations have led to the emergent need for a dedicated architectural framework to support large-scale computing in the health informatics domain. The application areas of large-scale computing in health informatics include real-time predictive analytics, risk stratification, patient cohort analytics, development of predictive models for specific institutions or population groups, and many more. The use of EHR data for a multitude of decision-making processes in both clinical and non-clinical settings has prompted the establishment of policies prescribing the conditions of access and use of EHR data for non-employed individuals in the organization. Consequently, the demand for accessing, using, and managing EHR data at scale has impacted the over.