Combining high-performance hardware, cloud computing, and deep learning frameworks to accelerate physical simulations: probing the Hopfield network

Vavilala, Vaibhav

doi:10.1088/1361-6404/ab7027

Cited by 4 publications

(2 citation statements)

References 26 publications

(46 reference statements)

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“…The research results of Vavilala [16] demonstrate the synthesis of high-performance computations, cloud computational servers (e.g., Google Colab), and high-performance platforms for deep learning (e.g., PyTorch). Using the Hopfield network as an example, the author demonstrates the capabilities of computer modeling for physical experiments and faster learning due to computer modeling of physical processes.…”

Section: Literature Reviewmentioning

confidence: 99%

Some aspects of the use of cloud computing in the training of physics teachers

Velychko¹,

Fedorenko²,

Kaidan³

et al. 2022

Educ. Dimens.

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The specifics of the training of physics teachers lies in the extensive use of experimental research. E-learning, the role of which has increased due to quarantine restrictions, has the means of experimental research in its arsenal. The international educational community has developed and uses a variety of electronic educational resources, which are integrated into collections and libraries. The professional training of physics teachers is not an exception. Electronic educational resources make it possible not only to deliver educational material in a variety of ways, sometimes impossible without ICT, but also to simulate events and processes for their wider study in educational purposes. The use of cloud calculations in the form of virtual laboratory workshops is a way to solve existing problems in the training of future physics teachers. The use of cloud calculations in the educational activities of future physics teachers contributes not only to the improvement of educational material but also to the improvement of digital competence of all participants of the educational process. The research analyzed the available virtual laboratory practices, the scheme of using physics simulations was suggested, the examples of their use during physics teaching and the role of cloud calculations in the training of physics teachers were given. An analysis of the use of cloud computing and the best practices of this activity is carried out.

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Section: Literature Reviewmentioning

confidence: 99%

Some aspects of the use of cloud computing in the training of physics teachers

Velychko¹,

Fedorenko²,

Kaidan³

et al. 2022

Educ. Dimens.

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“…The ideas to provide such resources via Internet were elaborated in the past [1]. Now a convenient cloud computing services appeared, and they are considered as computational resources along with the other possibilities [2]. Cloud computing is applied for various resource-consuming calculations, such as overlay analysis [3] or motion capture [4].…”

Section: Introductionmentioning

confidence: 99%

Towards Hardware OpenGL Support in Cloud Computing

Denisov¹,

Kalugina²

2021

Proceedings of the 31th International Conference on Computer Graphics and Vision. Volume 2

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Modern realistic computer graphics applications, such as physically accurate lighting simulation systems, require a lot of computer power for images generation. Usage of the resources of cloud computing platforms for such calculations allows to avoid additional expenses for purchase and maintenance of own computer farms. However often such simulation systems use OpenGL for 3D images display, for example during scene preparation and modification. Since cloud-based virtual machines had only software (that is, slow) OpenGL display support, it was not convenient for the users to work with their habitual computer graphics applications in such environments, and typical workflow was to prepare all data on local computer and then execute simulation in the cloud service (usually using distributed processing). Recently several cloud computer service providers started to suggest users the option of hardware (GPU-based) OpenGL support in their cloud virtual machines. This article is devoted to the investigation of hardware OpenGL display options, suggested by various providers of cloud computing services, and their comparison. Available types of hardware GPU were checked and compared, along with the conditions for their usage.

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Reduced-order modelling for real-time physics-based variation simulation enhanced with adaptive sampling and optimized interpolation

Russo,

Franciosa,

Greco

et al. 2024

Int J Adv Manuf Technol

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Manufacturing industries require a right-first-time paradigm to remain competitive. Variation simulation (VS) is a key tool to predict variation of the final shape of flexible assemblies, allowing to reduce defects and waste. VS models involving compliant sheet-metal parts commonly integrate physics-based simulation with statistical approaches (usually Monte Carlo simulation). Although increasingly used as a backbone of synthesis techniques for (stochastic) optimization of assembly systems, the main roadblock of VS methods is the intense computational costs due to time-intensive simulations and high-dimensional design space. Driven by the need of time reduction, this paper presents an innovative real-time physics-based VS model of assembly systems with compliant sheet-metal parts. The proposed methodology involves a non-intrusive reduced-order model (niROM), empowered by a novel adaptive sampling procedure for dataset generation, and a cross-validation-based optimized radial basis function (RBF) formulation for interpolation. Demonstrated through two case studies—(i) a remote laser welding operation to predict mechanical distortions, with two input parameters, and (ii) the assembly of an aircraft vertical stabilizer with five input parameters—the methodology achieves accurate real-time results, with up to a 43% improvement in accuracy compared to traditional sampling techniques. Findings highlight the critical influence of the sampling strategy and the number of input parameters on ROM accuracy. Better results are reached by employing adaptive sampling in combination with optimum RBF, which additionally disengages the user from the choice of the interpolation settings. This study unlocks new avenues in the field of variation simulation and dimensional/quality monitoring by narrowing the gap between any advanced CAE solver and VS models with real-time physics-based simulations.

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Combining high-performance hardware, cloud computing, and deep learning frameworks to accelerate physical simulations: probing the Hopfield network

Cited by 4 publications

References 26 publications

Some aspects of the use of cloud computing in the training of physics teachers

Some aspects of the use of cloud computing in the training of physics teachers

Towards Hardware OpenGL Support in Cloud Computing

Reduced-order modelling for real-time physics-based variation simulation enhanced with adaptive sampling and optimized interpolation

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