Evaluation of next-generation high-order compressible fluid dynamic solver on cloud computing for complex industrial flows

Jahdali, Rasha Al; Kortas, S.; Shaikh, M.; Dalcín, Lisandro; Parsani, Matteo

doi:10.1016/j.array.2022.100268

Cited by 4 publications

(1 citation statement)

References 30 publications

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“…Regarding OpenAI tools, GPT-3 (Generative Pretrained Transformer 3) is used for its advanced natural language processing capacity, essential for generating realistic cybersecurity scenarios and interactively dialogue with users. Additionally, DALL-E is innovatively used to create data visualizations and graphical representations, making it easier to understand complex cybersecurity concepts [32]. The synergy between AWS's scalable and secure infrastructure and OpenAI's language processing and content generation capabilities form the foundation of our development environment.…”

Section: Description Of the Development Environmentmentioning

confidence: 99%

Developing a Cybersecurity Training Environment through the Integration of OpenAI and AWS

Villegas-Ch,

Govea,

Ortiz-Garces

2024

Applied Sciences

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Cybersecurity is a critical concern in today’s digital age, where organizations face an ever-evolving cyber threat landscape. This study explores the potential of leveraging artificial intelligence and Amazon Web Services to improve cybersecurity practices. Combining the capabilities of OpenAI’s GPT-3 and DALL-E models with Amazon Web Services infrastructure aims to improve threat detection, generate high-quality synthetic training data, and optimize resource utilization. This work begins by demonstrating the ability of artificial intelligence to create synthetic cybersecurity data that simulates real-world threats. These data are essential for training threat detection systems and strengthening an organization’s resilience against cyberattacks. While our research shows the promising potential of artificial intelligence and Amazon Web Services in cybersecurity, it is essential to recognize the limitations. Continued research and refinement of AI models are needed to address increasingly sophisticated threats. Additionally, ethical and privacy considerations must be addressed when employing AI in cybersecurity practices. The results support the notion that this collaboration can revolutionize how organizations address cyber challenges, delivering greater efficiency, speed, and accuracy in threat detection and mitigation.

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Section: Description Of the Development Environmentmentioning

confidence: 99%

Developing a Cybersecurity Training Environment through the Integration of OpenAI and AWS

Villegas-Ch,

Govea,

Ortiz-Garces

2024

Applied Sciences

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Application of maximum statistical entropy in formulating a non-gaussian probability density function in flow uncertainty analysis with prior measurement knowledge

Ramnath

2024

Int. J. Metrol. Qual. Eng.

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In mechanical, civil and chemical engineering systems the accuracies of flow measurement instruments is conventionally specified by certified measurement capabilities (CMCs) that are symmetric, however it is physically possible for some flow instruments and equipment to exhibit asymmetric non-Gaussian behaviour. In this paper the influence of non-Gaussian uncertainties is investigated using direct Monte Carlo simulations to construct a probability density function (PDF) using representative non-Gaussian surface roughness data for a commercial steel pipe friction factor. Actual PDF results are compared and contrasted with a symmetric Gaussian PDF, and reveal inconsistencies in the statistical distributions that cannot be neglected in high accuracy flow measurements. The non-Gaussian PDF is visualized with a kernel density estimate (KDE) scheme to infer an initial qualitative shape of the actual PDF using the approximate locations of the normalized peaks as a initial metrologist estimate of the measurement density. This is then utilized as inputs in a maximum statistical entropy functional to optimize the actual non-Gaussian PDF using a nonlinear optimization of Lagrange multipliers for a mathematically unique PDE. Novelties in the present study is that a new methodology has been developed for statistical sampling from non-monotonic non-Gaussian distributions with accompanying Python and Matlab/GNU Octave computer codes, and a new methodology for utilizing metrologist's expert prior knowledge of PDF peaks and locations for constructing an a priori estimate of the shape of unknown density have been incorporated into the maximum statistical entropy nonlinear optimization problem for a faster and more efficient approach for generating statistical information and insights in constructing high accuracy non-Gaussian PDFs of real world messy engineering measurements.

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Performance Analysis of Computational Fluid Dynamics Workloads on HPC in the Cloud

Kumar

2023

2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)

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Evaluation of next-generation high-order compressible fluid dynamic solver on cloud computing for complex industrial flows

Cited by 4 publications

References 30 publications

Developing a Cybersecurity Training Environment through the Integration of OpenAI and AWS

Developing a Cybersecurity Training Environment through the Integration of OpenAI and AWS

Application of maximum statistical entropy in formulating a non-gaussian probability density function in flow uncertainty analysis with prior measurement knowledge

Performance Analysis of Computational Fluid Dynamics Workloads on HPC in the Cloud

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