A real-time big data analysis framework on a CPU/GPU heterogeneous cluster

Hassaan, M. Y.; Elghandour, Iman

doi:10.1145/3006299.3006304

Cited by 7 publications

(2 citation statements)

References 15 publications

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“…In recent decades, the skyrocketing usage of big data, assisted by advanced computing technologies such as GPU computing [20], has led to the enhancement of machine learning algorithms-specifically, deep neural networks [21]. Deep neural networks can learn and capture features from highly nonlinear data for accurate predictions, indicating their huge potential and attracting a great deal of attention in various fields.…”

Section: Introductionmentioning

confidence: 99%

Controlling Chaos in Van Der Pol Dynamics Using Signal-Encoded Deep Learning

Zhai

Sands

2022

Mathematics

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Controlling nonlinear dynamics is a long-standing problem in engineering. Harnessing known physical information to accelerate or constrain stochastic learning pursues a new paradigm of scientific machine learning. By linearizing nonlinear systems, traditional control methods cannot learn nonlinear features from chaotic data for use in control. Here, we introduce Physics-Informed Deep Operator Control (PIDOC), and by encoding the control signal and initial position into the losses of a physics-informed neural network (PINN), the nonlinear system is forced to exhibit the desired trajectory given the control signal. PIDOC receives signals as physics commands and learns from the chaotic data output from the nonlinear van der Pol system, where the output of the PINN is the control. Applied to a benchmark problem, PIDOC successfully implements control with a higher stochasticity for higher-order terms. PIDOC has also been proven to be capable of converging to different desired trajectories based on case studies. Initial positions slightly affect the control accuracy at the beginning stage yet do not change the overall control quality. For highly nonlinear systems, PIDOC is not able to execute control with a high accuracy compared with the benchmark problem. The depth and width of the neural network structure do not greatly change the convergence of PIDOC based on case studies of van der Pol systems with low and high nonlinearities. Surprisingly, enlarging the control signal does not help to improve the control quality. The proposed framework can potentially be applied to many nonlinear systems for nonlinear controls.

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Section: Introductionmentioning

confidence: 99%

Controlling Chaos in Van Der Pol Dynamics Using Signal-Encoded Deep Learning

Zhai

Sands

2022

Mathematics

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“…In the past decades, the skyrocketing big data, assisted with advanced computing technologies such as GPU computing [21], incited the development of machine learning algorithms, specifically, deep neural networks [20], as they can learn and capture features from highly nonlinear data for accurate predictions, showing huge potentials that attracted public's attention in various fields. In recent years, utilizing physics information to encode in the losses of a deep neural network (NN), promising faster accurate learning of physics with NN that respect basic physics laws with less labeled data, commonly recognized as Physics-Informed Neural Networks (PINNs) [22,23].…”

Section: Inductormentioning

confidence: 99%

Physics-Informed Deep Operator Control: Controlling chaos in van der Pol oscillating circuits

Zhai,

Sands

2021

Preprint

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Controlling chaos is a long-standing problem in engineering. By linearizing nonlinear systems, traditional control methods cannot learn features from chaotic data for control. Here, we introduce Physics-Informed Deep Operator Control (PIDOC), by encoding control signal and initial position into the losses of a Physics-Informed Neural Network (PINN), the nonlinear system exhibits the desired route given the control signal. The framework has a clear application scenario: receiving signals as physics commands and learning chaotic data from the nonlinear van der Pol system, PIDOC is able to execute the controlled signal as the output of the PINN. PIDOC is first applied to a benchmark problem, unveils a fluctuating behavior of the acceleration that seemingly behave in the same frequency of the desired signal, indicating the learning of neural networks (NN) elicit stochasticity for higher-order terms, yet still successfully impose the control that enforces the system behave as in the same frequency of the control signal. PIDOC is also proved capable of converging to different desired trajectories based on case studies. Initial positions slightly affect the control accuracy at the beginning stage yet still converge to the control signal based on numerical experiments. To note, for highly nonlinear systems, PIDOC is not able to execute control as high accuracies compared with the benchmark problem, yet still, PIDOC converges to decent trajectories corresponding to given signals with fair predictable behavior. The depth and width of the NN structure did not heavily variate the convergence of PIDOC based on case studies on van der Pol systems with low and high nonlinearities. Surprisingly, by enlarging the control signal by multiplying a Lagrangian multiplier, PIDOC failed to converge to the desired trajectory, indicating enlarging the control signal does not increase control quality, which is not intuitive. The proposed framework can be potentially applied to many nonlinear systems for controlling chaos.

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Hoel¹

2018

Encyclopedia of Big Data Technologies

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A real-time big data analysis framework on a CPU/GPU heterogeneous cluster

Cited by 7 publications

References 15 publications

Controlling Chaos in Van Der Pol Dynamics Using Signal-Encoded Deep Learning

Controlling Chaos in Van Der Pol Dynamics Using Signal-Encoded Deep Learning

Physics-Informed Deep Operator Control: Controlling chaos in van der Pol oscillating circuits

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